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Introduction, theoretical predictions, what the empirical evidence says, empirical challenges, acknowledgments.

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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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Rising inequality affecting more than two-thirds of the globe, but it’s not inevitable: new UN report

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Inequality is growing for more than 70 per cent of the global population, exacerbating the risks of divisions and hampering economic and social development. But the rise is far from inevitable and can be tackled at a national and international level, says a flagship study released by the UN on Tuesday.

The World Social Report 2020, published by the UN Department of Economic and Social Affairs (DESA), shows that income inequality has increased in most developed countries, and some middle-income countries - including China, which has the world’s fastest growing economy.

The challenges are underscored by UN chief António Guterres in the foreword, in which he states that the world is confronting “the harsh realities of a deeply unequal global landscape”, in which economic woes, inequalities and job insecurity have led to mass protests in both developed and developing countries.

 “Income disparities and a lack of opportunities”, he writes, “are creating a vicious cycle of inequality, frustration and discontent across generations.”

‘The one per cent’ winners take (almost) all

The study shows that the richest one per cent of the population are the big winners in the changing global economy, increasing their share of income between 1990 and 2015, while at the other end of the scale, the bottom 40 per cent earned less than a quarter of income in all countries surveyed.

One of the consequences of inequality within societies, notes the report, is slower economic growth. In unequal societies, with wide disparities in areas such as health care and education, people are more likely to remain trapped in poverty, across several generations.

Between countries, the difference in average incomes is reducing, with China and other Asian nations driving growth in the global economy. Nevertheless, there are still stark differences between the richest and poorest countries and regions: the average income in North America, for example, is 16 times higher than that of people in Sub-Saharan Africa.

Four global forces affecting inequality

The report looks at the impact that four powerful global forces, or megatrends, are having on inequality around the world: technological innovation, climate change, urbanization and international migration.

Whilst technological innovation can support economic growth, offering new possibilities in fields such as health care, education, communication and productivity, there is also evidence to show that it can lead to increased wage inequality, and displace workers.

Rapid advances in areas such as biology and genetics, as well as robotics and artificial intelligence, are transforming societies at pace. New technology has the potential to eliminate entire categories of jobs but, equally, may generate entirely new jobs and innovations.

For now, however, highly skilled workers are reaping the benefits of the so-called “fourth industrial revolution”, whilst low-skilled and middle-skilled workers engaged in routine manual and cognitive tasks, are seeing their opportunities shrink.

Opportunities in a crisis

As the UN’s 2020 report on the global economy showed last Thursday, the climate crisis is having a negative impact on quality of life, and vulnerable populations are bearing the brunt of environmental degradation and extreme weather events. Climate change, according to the World Social Report, is making the world’s poorest countries even poorer, and could reverse progress made in reducing inequality among countries.

If action to tackle the climate crisis progresses as hoped, there will be job losses in carbon-intensive sectors, such as the coal industry, but the “greening” of the global economy could result in overall net employment gains, with the creation of many new jobs worldwide.

For the first time in history, more people live in urban than rural areas, a trend that is expected to continue over the coming years. Although cities drive economic growth, they are more unequal than rural areas, with the extremely wealthy living alongside the very poor.

The scale of inequality varies widely from city to city, even within a single country: as they grow and develop, some cities have become more unequal whilst, in others, inequality has declined.

Migration a ‘powerful symbol of global inequality’

The fourth megatrend, international migration, is described as both a “powerful symbol of global inequality”, and “a force for equality under the right conditions”.

Migration within countries, notes the report, tends to increase once countries begin to develop and industrialize, and more inhabitants of middle-income countries than low-income countries migrate abroad.

International migration is seen, generally, as benefiting both migrants, their countries of origin (as money is sent home) and their host countries.

In some cases, where migrants compete for low-skilled work, wages may be pushed down, increasing inequality but, if they offer skills that are in short supply, or take on work that others are not willing to do, they can have a positive effect on unemployment.

Harness the megatrends for a better world

Despite a clear widening of the gap between the haves and have-nots worldwide, the report points out that this situation can be reversed. Although the megatrends have the potential to continue divisions in society, they can also, as the Secretary-General says in his foreword, “be harnessed for a more equitable and sustainable world”. Both national governments and international organizations have a role to play in levelling the playing field and creating a fairer world for all.

Reducing inequality should, says the report, play a central role in policy-making. This means ensuring that the potential of new technology is used to reduce poverty and create jobs; that vulnerable people grow more resilient to the effects of climate change; cities are more inclusive; and migration takes place in a safe, orderly and regular manner.

Three strategies for making countries more egalitarian are suggested in the report: the promotion of equal access to opportunities (through, for example, universal access to education); fiscal policies that include measures for social policies, such as unemployment and disability benefits; and legislation that tackles prejudice and discrimination, whilst promoting greater participation of disadvantaged groups.

While action at a national level is crucial, the report declares that “concerted, coordinated and multilateral action” is needed to tackle major challenges affecting inequality within and among countries.

The report’s authors conclude that, given the importance of international cooperation, multilateral institutions such as the UN should be strengthened and action to create a fairer world must be urgently accelerated.

The UN’s 2030 Agenda for Sustainable Development , which provides the blueprint for a better future for people and the planet, recognizes that major challenges require internationally coordinated solutions, and contains concrete and specific targets to reduce inequality, based on income.

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The Roots of Economic Inequality

A new study shows that deeply ingrained social behaviors play a role in perpetuating economic inequality. Yale SOM’s Michael Kraus, one of the authors of the study, says we all need to mind the widening gap between rich and poor.

Photo: Joanne Kim

• Michael W. Kraus Associate Professor of Organizational Behavior

The United States is divided by wealth and social class, and the gap is widening.

In 1980, according to the World Inequality Report, the richest 10% of the population held just under 35% of national income; by 2016, that share had risen to around 47%. As wealth disparities have widened, so have differences in outcomes. The rich now have a hugely better chance at educational attainment, good health, and even longer life expectancy than the poor.

Economic inequality is rising despite the fact that most people agree things should be shared more equally, says Michael Kraus, assistant professor of organizational behavior at the Yale School of Management. So why is it so hard to change the systems that perpetuate the problem?

Kraus, UC Irvine’s Paul Piff and UC Berkeley’s Dacher Keltner investigated this question through the lens of psychology. In a paper in Advances in Experimental Social Psychology , the authors put forth a conceptual model, drawing on existing research, to show how individuals maintain inequality through their actions and beliefs. The authors look at five domains of social life—structural barriers, social class signaling, ideologies of merit, moral-relational tendencies, and intergroup processes—and detail how they perpetuate class divisions in everyday life.

Read the study: “Unpacking the Inequality Paradox: The Psychological Roots of Inequality and Social Class ”

The study as a whole, Kraus says, shows that even “mundane, everyday beliefs and behaviors are contributing to rising inequality.” What we wear and how we speak, for example, can determine whether an employer hires us or not, whether we succeed in networking or not. And such class signaling happens fast; in one study the authors cite, and which Kraus conducted, participants from across the U.S. successfully guessed a speaker’s social class after hearing them speak just seven words, out of context . “And that’s a problem for a job interview,” says Kraus. “It might be a problem for an admissions interview for college, and it certainly could create socialization challenges if you go to college and you’re speaking a different way than some of the other students.”

How we think about merit and hard work hinders the advancement of working class individuals and is highly advantageous to upper class ones, the authors write. We’re quite blind to just how unequal our society is, and “a lot of that thinking is driven by broader beliefs that life has to be fair and it’s getting more fair,” says Kraus. But that is simply not the case, as economic indicators show. Upper-class individuals are more likely to believe wealth is primarily a product of hard work and effort, which generally isn’t the case; inherited wealth has an immense impact on social class. But this idea—the deeply held belief that we live in a meritocracy—means that people born to lower-class families are blamed for their poverty and face an additional headwind when trying to catch up.

While the authors look at the psychological roots of inequality, “one of the dangers here,” says Kraus, “is to say that inequality exists in people’s minds.” There are objective, structural barriers that perpetuate inequality, and reinforce our flawed thinking about it, the authors write. Universities are a good example. At elite institutions, poor students are usually a minority; surrounded by classmates—and taught by professors—from middle- and upper-class backgrounds, their academic performance is impeded by anxiety about whether they belong and whether failure will confirm stereotypes about their class. One study suggested that this anxiety is well founded: evaluators found more mistakes in work from students that they believed were lower in social class. Meanwhile, the same structures that disadvantage the poor afford upper-class individuals a leg up. The wealthy benefit and then award the university or other institutions with more resources, continuing a cycle that rewards the wealthy.

The study’s findings aren’t easy to swallow, says Kraus. “Maybe we’re a little too optimistic about how things will get better automatically. Maybe we need to acknowledge our collective part in perpetuating systems of inequality.”

But that’s why more psychologists should be studying inequality. “It’s powerful to get psychologists to talk about and underscore all the ways in which everyday interactions and psychological processes that people engage in can be in the service of big economic patterns,” says Kraus. “We are really motivated to keep things the way they are, and to change, we need to start with an acknowledgement that things are off.”

More research can help. For instance, studies that combat ideologies of merit and that raise awareness of the racial patterns of economic inequality help address the psychological roots of inequality by changing people’s thinking on the topic.

But real change will require policy solutions. A more equal society “relies on us creating structures and bureaucracy that allow us to overcome our biases,” Kraus says. He and his colleagues call on the U.S. to adopt “large-scale government programs” and policy changes to reduce structural barriers to equality, including better access to education, a higher minimum wage, and a universal basic income.

While a challenging road lies ahead, Kraus considers the study a good starting point. “We have to acknowledge the real work necessary to create meaningful change,” he says. That starts with understanding just how pervasive inequality is.

Related: Watch a Yale Insights interview with Michael Kraus on how Americans misperceive progress toward racial economic equality.

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6 facts about economic inequality in the U.S.

Rising economic inequality in the United States has become a central issue in the race for the Democratic presidential nomination, and discussions about policy interventions that might help address it are likely to remain at the forefront in the 2020 general election .

As these debates continue, here are some basic facts about how economic inequality has changed over time and how the U.S. compares globally.

How we did this

For this analysis, we gathered data from the U.S. Census Bureau, Organization for Economic Cooperation and Development and the World Bank . We also used previously published data points from Pew Research Center surveys and analyses of outside data.

Over the past 50 years, the highest-earning 20% of U.S. households have steadily brought in a larger share of the country’s total income. In 2018, households in the top fifth of earners (with incomes of $130,001 or more that year) brought in 52% of all U.S. income, more than the lower four-fifths combined, according to Census Bureau data.

In 1968, by comparison, the top-earning 20% of households brought in 43% of the nation’s income, while those in the lower four income quintiles accounted for 56%.

Among the top 5% of households – those with incomes of at least $248,729 in 2018 – their share of all U.S. income rose from 16% in 1968 to 23% in 2018.

Income inequality in the U.S. is the highest of all the G7 nations , according to data from the Organization for Economic Cooperation and Development . To compare income inequality across countries, the OECD uses the Gini coefficient , a commonly used measure ranging from 0, or perfect equality, to 1, or complete inequality. In 2017, the U.S. had a Gini coefficient of 0.434. In the other G7 nations, the Gini ranged from 0.326 in France to 0.392 in the UK.

Globally, the Gini ranges from lows of about 0.25 in some Eastern European countries to highs of 0.5 to 0.6 in countries in southern Africa, according to World Bank estimates .

The black-white income gap in the U.S. has persisted over time. The difference in median household incomes between white and black Americans has grown from about $23,800 in 1970 to roughly $33,000 in 2018 (as measured in 2018 dollars). Median black household income was 61% of median white household income in 2018, up modestly from 56% in 1970 – but down slightly from 63% in 2007, before the Great Recession , according to Current Population Survey data.

Overall, 61% of Americans say there is too much economic inequality in the country today, but views differ by political party and household income level. Among Republicans and those who lean toward the GOP, 41% say there is too much inequality in the U.S., compared with 78% of Democrats and Democratic leaners, a Pew Research Center survey conducted in September 2019 found.

Across income groups, U.S. adults are about equally likely to say there is too much economic inequality. But upper- (27%) and middle-income Americans (26%) are more likely than those with lower incomes (17%) to say that there is about the right amount of economic inequality.

These views also vary by income within the two party coalitions. Lower-income Republicans are more likely than upper-income ones to say there’s too much inequality in the country today (48% vs. 34%). Among Democrats, the reverse is true: 93% at upper-income levels say there is too much inequality, compared with 65% of lower-income Democrats.

The wealth gap between America’s richest and poorer families more than doubled from 1989 to 2016, according to a recent analysis by the Center. Another way of measuring inequality is to look at household wealth, also known as net worth, or 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.

In 1989, the richest 5% of families had 114 times as much wealth as families in the second quintile (one tier above the lowest), at the median $2.3 million compared with $20,300. By 2016, the top 5% held 248 times as much wealth at the median. (The median wealth of the poorest 20% is either zero or negative in most years we examined.)

The richest families are also the only ones whose wealth increased in the years after the start of the Great Recession. From 2007 to 2016, the median net worth of the top 20% increased 13%, to $1.2 million. For the top 5%, it increased by 4%, to $4.8 million. In contrast, the median net worth of families in lower tiers of wealth decreased by at least 20%. Families in the second-lowest fifth experienced a 39% loss (from $32,100 in 2007 to $19,500 in 2016).

Middle-class incomes have grown at a slower rate than upper-tier incomes over the past five decades, the same analysis found . From 1970 to 2018, the median middle-class income increased from $58,100 to $86,600, a gain of 49%. By comparison, the median income for upper-tier households grew 64% over that time, from $126,100 to $207,400.

The share of American adults who live in middle-income households has decreased from 61% in 1971 to 51% in 2019. During this time, the share of adults in the upper-income tier increased from 14% to 20%, and the share in the lower-income tier increased from 25% to 29%.

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Katherine Schaeffer is a research analyst at Pew Research Center .

Income inequality is greater among Chinese Americans than any other Asian origin group in the U.S.

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The history of global economic inequality

The inequality in people’s living conditions across the world is extremely large. how did the world become so unequal, and what can we expect for the future.

This article was originally published in 2017. The data shown and discussed in the text are therefore not always the latest estimates. For more up-to-date data, see our Data Explorers on Poverty and Inequality .

The inequality in people’s living conditions today is extremely large. The panel of charts below shows how large these differences are, and how the inequality in 12 important measures of living standards maps onto the economic inequality in the world.

What is most important for how healthy, wealthy, and educated you are is not who you are, but where you are. This was the point I made in another article . A person’s knowledge, skills, and how hard they work all matter for whether they are poor or not – but all these personal factors together matter less than the one factor that is entirely outside of a person’s control: whether they happen to be born into a large, productive economy or not.

How did we get here? How did the world become so unequal, and what can we expect for the future?

Global divergence followed by convergence

The chart shows estimates of the distribution of annual income among all world citizens over the last two centuries.

To make incomes comparable across countries and time, daily incomes are measured in international-$ — a hypothetical currency that would buy a comparable amount of goods and services that a U.S. dollar would buy in the United States in 2011 (for a more detailed explanation, see here ).

The distribution of incomes is shown at 3 points in time:

• By 1800, few countries had achieved economic growth. The chart shows that most of the world lived in poverty with an income similar to today's poorest countries. At the beginning of the 19th century, the vast majority— roughly 80% —of the world lived in material conditions that we would refer to as extreme poverty today.
• In 1975, 175 years later, the world had changed—it had become very unequal. The world income distribution was 'bimodal', with the two-humped shape of a camel: one hump below the international poverty line and a second hump at considerably higher incomes. The world had divided into a poor, developing world and a developed world more than 10-times richer.
• Over the following 4 decades, the world income distribution has again changed dramatically. There has been a convergence in incomes: in many poorer countries, especially in South-East Asia, incomes have grown faster than in rich countries. While enormous income differences remain, the world can no longer be neatly divided into 'developed' and 'developing' countries. We have moved from a two-hump to a one-hump world. And at the same time, the distribution has also shifted to the right—the incomes of many of the world's poorest citizens have increased, and extreme poverty has fallen faster than ever before in human history.

We have visualized a similar dataset from the OECD here . 1

Global income inequality increased for two centuries and is now falling

This visualization shows the distribution of incomes between 1988 and 2011. The data was compiled by the economists Branko Milanovic and Christoph Lakner. 3

To see the change over time, select the years above the distribution.

The previous visualization, which showed the change from 1820 to the year 2015, is based on estimates of inflation-adjusted average incomes per country (GDP per capita) and a measure of income inequality within a country only. It gives us a rough idea of how the distribution of incomes changed, but it is not very detailed or precise. In contrast to this, the work by Branko Milanovic and Christoph Lakner is based on much more detailed household survey data. This data measures household income at each decile of the income distribution, and the two authors used this information to arrive at the global income distribution. The downside of this approach is that we can only go as far back in time as household surveys were conducted in many countries around the world.

The visualization shows the end of the long era in human history in which global inequality was increasing. Starting with industrialization in North-Western Europe, incomes in this part of the world started to increase while material prosperity in the rest of the world remained low. While some countries followed European industrialization – first Northern America, Oceania, and parts of South America and later Japan and East Asia – other countries in Asia and Africa remained poor. As a consequence of this, global inequality increased over a long period. Only in the period shown in this visualization did this change: with rapid growth in much of Asia and Latin America, the global distribution of incomes became less unequal. The incomes of the poorer half of the world population rose faster than the incomes of the richer half.

Global Income Distribution 1988 to 2011 3

If you want to use this visualization for a presentation or teaching purposes, download a zip folder with an image file for every year and an animated .gif here .

A look into the future of global inequality

This visualization shows how the global income distribution has changed over the decade up to 2013. Tomáš Hellebrandt and Paolo Mauro, the authors of the paper 4 from which this data is taken, confirm the finding that global inequality has declined but remains very high: the Gini coefficient of global inequality has declined from 68.7 to 64.9.

The visualizations above show the income distribution on a logarithmic x-axis. This chart, in contrast, plots incomes on a linear x-axis and thereby emphasizes how very high global inequality still is: The bulk of the world population lives on very low incomes, and the income distribution stretches out very far to the higher incomes at the right-hand side of the chart; incomes over 14,000 international-$ are cut off as they would make this chart with a linear x-axis unreadable.

A second positive global development shown in this chart is the rise of the global median income. In 2003 half of the world's population lived on less than 1,090 international-$ per year, and the other half lived on more than 1,090 international-$. This level of global median income has almost doubled over the last decade and was 2,010 international-$ in 2013.

Finally, the authors also dare to project what global inequality will look like in 2035. Assuming the growth rates shown in the insert in the top-right corner, the authors project global inequality to decline further and to reach a Gini of 61.3. At the same time, the incomes of the world's poorer half would continue to increase significantly, so that the global median income could again double and reach 4,000 international-$ in 2035.

If you are looking for a visualization of only the observed global income distribution in 2003 and 2013, you can find it here .

How global inequality has changed from 2003 to 2013

The following visualization offers an alternative view of the data by Hellebrandt and Mauro 4 shown in the chart before.

The chart shows the yearly disposable income for all world citizens in both 2003 and 2013. On the x-axis, you see the position of an individual in the global distribution of incomes. On the logarithmic y-axis, you see the annual disposable income at that position.

The increase in prosperity—and decrease in poverty—is substantial. The income cut-off of the poorest 10% has increased from 260 international-$ to 480 international-%, and the median income has almost doubled from 1,100 international-$ to 2,010. Global mean income in 2013 was 5,375 international-$. 5

At the same it is still the case, as emphasized before, that incomes are very low for most people in the world.

Global income inequality is very high and will likely stay high for a long time

The visualization presents the same data in the same way, except that the y-axis is now not logarithmic but linear. This perspective shows the still very high level of global inequality even more clearly.

The previous and the following visualization show how high global income inequality is. The cut-off to the richest 10% of the world in 2013 was 14,500 int-$; the cut-off for the poorest 10% was 480 int-$. The ratio is 30.2.

While global inequality is still very high, we live in a period of falling inequality. In 2003, this ratio was 37.6. The Gini coefficient has also fallen from 68.7 to 64.9.

Taking the historical experience as a guide for what is possible in the future, we have to conclude that global inequality will remain high for a long time. To understand this, we can ask how long it would take for those with incomes at the poorest 10% cutoff to achieve the current incomes of the richest 10% cutoff (14,500 international-$). This income level is roughly the level of GDP per capita above which the extreme poverty headcount gets close to 0% for most countries ( see here ).

Even under a very optimistic scenario, it will take several decades for the poorest regions to reach the income level of the global top 10%.

2% is roughly the growth rate that the richest countries of today experienced over the last decades ( see here ). We have seen that poorer countries can achieve faster growth, but we have not seen growth rates of more than 6% over a time frame as long as necessary to reach the level of the global 10% in such a short time. If the past is a good guide for the future, the world will likely be highly unequal for a long time.

How long does it take for incomes to grow from 480 int-$ to 14,500 int-$?

Inequality within countries and inequality between countries

Global inequality is driven by changes in both the inequality within countries, and the inequality between countries. This visualization shows how both of these changes determine the changing global inequality.

• Inequality within countries followed a U-shape pattern over the 20th century.
• Inequality between countries increased over 2 centuries and peaked in the 1980s, according to the data from Bourguignon and Morrison. Since then, inequality between countries has declined.

As is shown in this visualization, the inequality of income between different countries is much higher than the inequality within countries. The consequence of this is that the trend of global inequality is very much driven by what is happening to the inequality between countries.

I have taken the data for the visualization of the world income distribution in 1820, 1970, and 2000 from van Zanden, J.L., et al. (eds.) (2014), How Was Life?: Global Well-being since 1820, OECD Publishing. Online here . The plotted data is interpolated using a Cardinal spline.

The data is originally from the Clio-Infra database here .

The data are produced by Ola Rosling and published on the website of Gapminder.

You can explore the Gapminder visualization of the income distributions of all countries in their interactive tool here . Regarding the construction of the data, Hans and Ola Rosling note the following here : “This graph is constructed by combining data from multiple sources. In summary, we take the best available country estimates for the three indicators: GDP per capita, Population, and Gini coefficient (a measure of income inequality). With these numbers, we can approximate the number of people at different income levels in every country. We then combine all these approximations into a global pile using the method described below under The Adjusted Global Income Scale.”

The data was made available to Our World In Data by the two authors. The data up to 2008 is published with the main publication Milanovic and Lakner (2015) –Global Income Distribution. Available online at the World Bank: http://elibrary.worldbank.org/doi/abs/10.1596/1813-9450-6719 .

The data source is: Hellebrandt, Tomas and Mauro, Paolo (2015) – The Future of Worldwide Income Distribution (April 1, 2015). Peterson Institute for International Economics Working Paper No. 15-7. Available at SSRN or http://dx.doi.org/10.2139/ssrn.2593894 .

We thank the authors for making the data available for this data visualization.

Note that global GDP per capita in 2013 was around 14,000 international-$ and substantially higher than mean disposable income from household-level surveys at 5,375 international-$.

We discuss the reasons for this discrepancy here . See also the Appendix of the original publication for a longer explanation.

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Poverty Reduction: Concept, Approaches, and Case Studies

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Part of the book series: Encyclopedia of the UN Sustainable Development Goals ((ENUNSDG))

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Definitions

Poverty is universally measured in monetary expenditure terms, and individuals that are considered poor are those living on less than US$1.25 per day. Poverty is however multifaceted as it includes the multitude of lack and deprivations that poor people are subjected to in their lives on a daily basis. These include but are not limited to disease and poor health conditions, illiteracy and lack of access to education, appalling living conditions, lack of access to economic opportunity and disempowerment, underemployment, vulnerability to violence, and exposure to hazardous environmental conditions (OPHI 2019 ). Thus, poverty reduction can be considered as the improvement of an individual’s or group’s monetary expenditure to an amount above the poverty line while improving access to education, healthcare, information, economic opportunities security of land-tenure, and all the other deprivations associated with it.

Introduction

The eradication of poverty is perhaps the only...

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Bununu, Y.A. (2020). Poverty Reduction: Concept, Approaches, and Case Studies. In: Leal Filho, W., Azul, A., Brandli, L., Özuyar, P., Wall, T. (eds) Decent Work and Economic Growth. Encyclopedia of the UN Sustainable Development Goals. Springer, Cham. https://doi.org/10.1007/978-3-319-71058-7_31-1

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The costs of inequality: Education’s the one key that rules them all

When there’s inequity in learning, it’s usually baked into life, Harvard analysts say

Corydon Ireland

Harvard Correspondent

Third in a series on what Harvard scholars are doing to identify and understand inequality, in seeking solutions to one of America’s most vexing problems.

Before Deval Patrick ’78, J.D. ’82, was the popular and successful two-term governor of Massachusetts, before he was managing director of high-flying Bain Capital, and long before he was Harvard’s most recent Commencement speaker , he was a poor black schoolchild in the battered housing projects of Chicago’s South Side.

The odds of his escaping a poverty-ridden lifestyle, despite innate intelligence and drive, were long. So how did he help mold his own narrative and triumph over baked-in societal inequality ? Through education.

“Education has been the path to better opportunity for generations of American strivers, no less for me,” Patrick said in an email when asked how getting a solid education, in his case at Milton Academy and at Harvard, changed his life.

“What great teachers gave me was not just the skills to take advantage of new opportunities, but the ability to imagine what those opportunities could be. For a kid from the South Side of Chicago, that’s huge.”

If inequality starts anywhere, many scholars agree, it’s with faulty education. Conversely, a strong education can act as the bejeweled key that opens gates through every other aspect of inequality , whether political, economic , racial, judicial, gender- or health-based.

Simply put, a top-flight education usually changes lives for the better. And yet, in the world’s most prosperous major nation, it remains an elusive goal for millions of children and teenagers.

Plateau on educational gains

The revolutionary concept of free, nonsectarian public schools spread across America in the 19th century. By 1970, America had the world’s leading educational system, and until 1990 the gap between minority and white students, while clear, was narrowing.

But educational gains in this country have plateaued since then, and the gap between white and minority students has proven stubbornly difficult to close, says Ronald Ferguson, adjunct lecturer in public policy at Harvard Kennedy School (HKS) and faculty director of Harvard’s Achievement Gap Initiative. That gap extends along class lines as well.

“What great teachers gave me was not just the skills to take advantage of new opportunities, but the ability to imagine what those opportunities could be. For a kid from the South Side of Chicago, that’s huge.” — Deval Patrick

In recent years, scholars such as Ferguson, who is an economist, have puzzled over the ongoing achievement gap and what to do about it, even as other nations’ school systems at first matched and then surpassed their U.S. peers. Among the 34 market-based, democracy-leaning countries in the Organization for Economic Cooperation and Development (OECD), the United States ranks around 20th annually, earning average or below-average grades in reading, science, and mathematics.

By eighth grade, Harvard economist Roland G. Fryer Jr. noted last year, only 44 percent of American students are proficient in reading and math. The proficiency of African-American students, many of them in underperforming schools, is even lower.

“The position of U.S. black students is truly alarming,” wrote Fryer, the Henry Lee Professor of Economics, who used the OECD rankings as a metaphor for minority standing educationally. “If they were to be considered a country, they would rank just below Mexico in last place.”

Harvard Graduate School of Education (HGSE) Dean James E. Ryan, a former public interest lawyer, says geography has immense power in determining educational opportunity in America. As a scholar, he has studied how policies and the law affect learning, and how conditions are often vastly unequal.

His book “Five Miles Away, A World Apart” (2010) is a case study of the disparity of opportunity in two Richmond, Va., schools, one grimly urban and the other richly suburban. Geography, he says, mirrors achievement levels.

A ZIP code as predictor of success

“Right now, there exists an almost ironclad link between a child’s ZIP code and her chances of success,” said Ryan. “Our education system, traditionally thought of as the chief mechanism to address the opportunity gap, instead too often reflects and entrenches existing societal inequities.”

Urban schools demonstrate the problem. In New York City, for example, only 8 percent of black males graduating from high school in 2014 were prepared for college-level work, according to the CUNY Institute for Education Policy, with Latinos close behind at 11 percent. The preparedness rates for Asians and whites — 48 and 40 percent, respectively — were unimpressive too, but nonetheless were firmly on the other side of the achievement gap.

In some impoverished urban pockets, the racial gap is even larger. In Washington, D.C., 8 percent of black eighth-graders are proficient in math, while 80 percent of their white counterparts are.

Fryer said that in kindergarten black children are already 8 months behind their white peers in learning. By third grade, the gap is bigger, and by eighth grade is larger still.

According to a recent report by the Education Commission of the States, black and Hispanic students in kindergarten through 12th grade perform on a par with the white students who languish in the lowest quartile of achievement.

There was once great faith and hope in America’s school systems. The rise of quality public education a century ago “was probably the best public policy decision Americans have ever made because it simultaneously raised the whole growth rate of the country for most of the 20th century, and it leveled the playing field,” said Robert Putnam, the Peter and Isabel Malkin Professor of Public Policy at HKS, who has written several best-selling books touching on inequality, including “Bowling Alone: The Collapse and Revival of the American Community” and “Our Kids: The American Dream in Crisis.”

Historically, upward mobility in America was characterized by each generation becoming better educated than the previous one, said Harvard economist Lawrence Katz. But that trend, a central tenet of the nation’s success mythology, has slackened, particularly for minorities.

“Thirty years ago, the typical American had two more years of schooling than their parents. Today, we have the most educated group of Americans, but they only have about .4 more years of schooling, so that’s one part of mobility not keeping up in the way we’ve invested in education in the past,” Katz said.

As globalization has transformed and sometimes undercut the American economy, “education is not keeping up,” he said. “There’s continuing growth of demand for more abstract, higher-end skills” that schools aren’t delivering, “and then that feeds into a weakening of institutions like unions and minimum-wage protections.”

“The position of U.S. black students is truly alarming.” — Roland G. Fryer Jr.

Fryer is among a diffuse cohort of Harvard faculty and researchers using academic tools to understand the achievement gap and the many reasons behind problematic schools. His venue is the Education Innovation Laboratory , where he is faculty director.

“We use big data and causal methods,” he said of his approach to the issue.

Fryer, who is African-American, grew up poor in a segregated Florida neighborhood. He argues that outright discrimination has lost its power as a primary driver behind inequality, and uses economics as “a rational forum” for discussing social issues.

Better schools to close the gap

Fryer set out in 2004 to use an economist’s data and statistical tools to answer why black students often do poorly in school compared with whites. His years of research have convinced him that good schools would close the education gap faster and better than addressing any other social factor, including curtailing poverty and violence, and he believes that the quality of kindergarten through grade 12 matters above all.

Supporting his belief is research that says the number of schools achieving excellent student outcomes is a large enough sample to prove that much better performance is possible. Despite the poor performance by many U.S. states, some have shown that strong results are possible on a broad scale. For instance, if Massachusetts were a nation, it would rate among the best-performing countries.

At HGSE, where Ferguson is faculty co-chair as well as director of the Achievement Gap Initiative, many factors are probed. In the past 10 years, Ferguson, who is African-American, has studied every identifiable element contributing to unequal educational outcomes. But lately he is looking hardest at improving children’s earliest years, from infancy to age 3.

In addition to an organization he founded called the Tripod Project , which measures student feedback on learning, he launched the Boston Basics project in August, with support from the Black Philanthropy Fund, Boston’s mayor, and others. The first phase of the outreach campaign, a booklet, videos, and spot ads, starts with advice to parents of children age 3 or younger.

“Maximize love, manage stress” is its mantra and its foundational imperative, followed by concepts such as “talk, sing, and point.” (“Talking,” said Ferguson, “is teaching.”) In early childhood, “The difference in life experiences begins at home.”

At age 1, children score similarly

Fryer and Ferguson agree that the achievement gap starts early. At age 1, white, Asian, black, and Hispanic children score virtually the same in what Ferguson called “skill patterns” that measure cognitive ability among toddlers, including examining objects, exploring purposefully, and “expressive jabbering.” But by age 2, gaps are apparent, with black and Hispanic children scoring lower in expressive vocabulary, listening comprehension, and other indicators of acuity. That suggests educational achievement involves more than just schooling, which typically starts at age 5.

Key factors in the gap, researchers say, include poverty rates (which are three times higher for blacks than for whites), diminished teacher and school quality, unsettled neighborhoods, ineffective parenting, personal trauma, and peer group influence, which only strengthens as children grow older.

“Peer beliefs and values,” said Ferguson, get “trapped in culture” and are compounded by the outsized influence of peers and the “pluralistic ignorance” they spawn. Fryer’s research, for instance, says that the reported stigma of “acting white” among many black students is true. The better they do in school, the fewer friends they have — while for whites who are perceived as smarter, there’s an opposite social effect.

The researchers say that family upbringing matters, in all its crisscrossing influences and complexities, and that often undercuts minority children, who can come from poor or troubled homes. “Unequal outcomes,” he said, “are from, to a large degree, inequality in life experiences.”

Trauma also subverts achievement, whether through family turbulence, street violence, bullying, sexual abuse, or intermittent homelessness. Such factors can lead to behaviors in school that reflect a pervasive form of childhood post-traumatic stress disorder.

[gz_sidebar align=”left”]

Possible solutions to educational inequality:

• Access to early learning
• Improved K-12 schools
• More family mealtimes
• Reinforced learning at home
• Data-driven instruction
• Longer school days, years
• Respect for school rules
• Small-group tutoring
• High expectations of students
• Safer neighborhoods

[/gz_sidebar]

At Harvard Law School, both the Trauma and Learning Policy Initiative and the Education Law Clinic marshal legal aid resources for parents and children struggling with trauma-induced school expulsions and discipline issues.

At Harvard Business School, Karim R. Lakhani, an associate professor who is a crowdfunding expert and a champion of open-source software, has studied how unequal racial and economic access to technology has worked to widen the achievement gap.

At Harvard’s Project Zero, a nonprofit called the Family Dinner Project is scraping away at the achievement gap from the ground level by pushing for families to gather around the meal table, which traditionally was a lively and comforting artifact of nuclear families, stable wages, close-knit extended families, and culturally shared values.

Lynn Barendsen, the project’s executive director, believes that shared mealtimes improve reading skills, spur better grades and larger vocabularies, and fuel complex conversations. Interactive mealtimes provide a learning experience of their own, she said, along with structure, emotional support, a sense of safety, and family bonding. Even a modest jump in shared mealtimes could boost a child’s academic performance, she said.

“We’re not saying families have to be perfect,” she said, acknowledging dinnertime impediments like full schedules, rudimentary cooking skills, the lure of technology, and the demands of single parenting. “The perfect is the enemy of the good.”

Whether poring over Fryer’s big data or Barendsen’s family dinner project, there is one commonality for Harvard researchers dealing with inequality in education: the issue’s vast complexity. The achievement gap is a creature of interlocking factors that are hard to unpack constructively.

Going wide, starting early

With help from faculty co-chair and Jesse Climenko Professor of Law Charles J. Ogletree, the Achievement Gap Initiative is analyzing the factors that make educational inequality such a complex puzzle: home and family life, school environments, teacher quality, neighborhood conditions, peer interaction, and the fate of “all those wholesome things,” said Ferguson. The latter include working hard in school, showing respect, having nice friends, and following the rules, traits that can be “elements of a 21st-century movement for equality.”

In the end, best practices to create strong schools will matter most, said Fryer.

He called high-quality education “the new civil rights battleground” in a landmark 2010 working paper for the Handbook of Labor Economics called “Racial Inequality in the 21st Century: The Declining Significance of Discrimination.”

Fryer tapped 10 large data sets on children 8 months to 17 years old. He studied charter schools, scouring for standards that worked. He champions longer school days and school years, data-driven instruction, small-group tutoring, high expectations, and a school culture that prizes human capital — all just “a few simple investments,” he wrote in the working paper. “The challenge for the future is to take these examples to scale” across the country.

How long would closing the gap take with a national commitment to do so? A best-practices experiment that Fryer conducted at low-achieving high schools in Houston closed the gap in math skills within three years, and narrowed the reading achievement gap by a third.

“You don’t need Superman for this,” he said, referring to a film about Geoffrey Canada and his Harlem Children’s Zone, just high-quality schools for everyone, to restore 19th-century educator Horace Mann’s vision of public education as society’s “balance-wheel.”

Last spring, Fryer, still only 38, won the John Bates Clark medal, the most prestigious award in economics after the Nobel Prize. He was a MacArthur Fellow in 2011, became a tenured Harvard professor in 2007, was named to the prestigious Society of Fellows at age 25. He had a classically haphazard childhood, but used school to learn, grow, and prosper. Gradually, he developed a passion for social science that could help him answer what was going wrong in black lives because of educational inequality.

With his background and talent, Fryer has a dramatically unique perspective on inequality and achievement, and he has something else: a seemingly counterintuitive sense that these conditions will improve, once bad schools learn to get better. Discussing the likelihood of closing the achievement gap if Americans have the political and organizational will to do so, Fryer said, “I see nothing but optimism.”

Correction: An earlier version of this story inaccurately portrayed details of Dr. Fryer’s background.

Illustration by Kathleen M.G. Howlett. Harvard staff writer Christina Pazzanese contributed to this report.

Next Tuesday: Inequality in health care

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Open Access

Peer-reviewed

Research Article

Tracking the impact of COVID-19 on economic inequality at high frequency

Contributed equally to this work with: Oriol Aspachs, Ruben Durante, Alberto Graziano, Josep Mestres, Marta Reynal-Querol, Jose G. Montalvo

Roles Conceptualization, Project administration, Supervision, Writing – original draft

Affiliation Caixabank Research, Caixabank, Barcelona, Catalonia, Spain

Roles Conceptualization, Funding acquisition, Investigation, Writing – original draft

Affiliations Department of Economics and Business, Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain, ICREA, Barcelona, Catalonia, Spain, Institute for Political Economy and Governance (IPEG), Barcelona, Catalonia, Spain, Barcelona Graduate School of Economics (BGSE), Barcelona, Catalonia, Spain

Roles Data curation, Formal analysis, Software

Roles Conceptualization, Data curation, Investigation, Software, Writing – original draft

Roles Funding acquisition, Investigation, Methodology

Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Software, Writing – original draft

* E-mail: [email protected]

Affiliations Department of Economics and Business, Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain, Institute for Political Economy and Governance (IPEG), Barcelona, Catalonia, Spain, Barcelona Graduate School of Economics (BGSE), Barcelona, Catalonia, Spain

• Oriol Aspachs, 
• Ruben Durante, 
• Alberto Graziano, 
• Josep Mestres, 
• Marta Reynal-Querol, 
• Jose G. Montalvo

• Published: March 31, 2021
• https://doi.org/10.1371/journal.pone.0249121
• Reader Comments

Pandemics have historically had a significant impact on economic inequality. However, official inequality statistics are only available at low frequency and with considerable delay, which challenges policymakers in their objective to mitigate inequality and fine-tune public policies. We show that using data from bank records it is possible to measure economic inequality at high frequency. The approach proposed in this paper allows measuring, timely and accurately, the impact on inequality of fast-unfolding crises, like the COVID-19 pandemic. Applying this approach to data from a representative sample of over three million residents of Spain we find that, absent government intervention, inequality would have increased by almost 30% in just one month. The granularity of the data allows analyzing with great detail the sources of the increases in inequality. In the Spanish case we find that it is primarily driven by job losses and wage cuts experienced by low-wage earners. Government support, in particular extended unemployment insurance and benefits for furloughed workers, were generally effective at mitigating the increase in inequality, though less so among young people and foreign-born workers. Therefore, our approach provides knowledge on the evolution of inequality at high frequency, the effectiveness of public policies in mitigating the increase of inequality and the subgroups of the population most affected by the changes in inequality. This information is fundamental to fine-tune public policies on the wake of a fast-moving pandemic like the COVID-19.

Citation: Aspachs O, Durante R, Graziano A, Mestres J, Reynal-Querol M, Montalvo JG (2021) Tracking the impact of COVID-19 on economic inequality at high frequency. PLoS ONE 16(3): e0249121. https://doi.org/10.1371/journal.pone.0249121

Editor: Shihe Fu, Xiamen University, CHINA

Received: September 18, 2020; Accepted: March 11, 2021; Published: March 31, 2021

Copyright: © 2021 Aspachs et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data cannot be shared publicly because they are owned by a third-party commercial bank (Caixabank), and there are legal restrictions to their use. The Legal Services of the bank accepted the use of the microdata only to researchers belonging to their Research Unit. Therefore, the researchers of the team that did not belong to Caixabank Research could not access the microdata. They contributed with the conceptualization of the research, the writing of code, the proposal of different empirical exercises and the writing of the manuscript. Data can be made available by Caixabank Research (contact via [email protected] ) to professional researchers who meet the criteria to access confidential data. Researchers interested in obtaining access to the data are required to submit a written application to Caixabank Research with a detailed research proposal consisting in a research question and motivation, information on the researcher CV, and a detailed explanation of the data needed, and the aggregation criteria to protect the anonymity of the registers. The authors will provide assistance to any researcher willing to analyze the data for replication purposes.

Funding: JGM & MRQ: ECO2017-82696P, Spanish Ministry of Science and Innovation ( http://www.ciencia.gob.es/ ); CEX 2019-000915S Severo Ochoa Program for Centers of Excellence ( http://www.ciencia.gob.es/ ). The Research Department of CaixaBank provided support in the form of salaries for authors OA, AG and, JM. The specific roles of these authors are articulated in the ‘author contributions’ section. JGM, MRQ and RD acknowledge the financial support of the "Ayudas Fundación BBVA a Equipos de Investigación Científica SARS-CoV-2 y COVID-19.” The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: OA, AG, and JM are employees of the Research Department of CaixaBank. There are no patents, products in development or marketed products to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction

The COVID-19 pandemic has had a massive impact on economic activity around the globe. To tackle the economic consequences of the pandemic, most governments have used a combination of family income support and credit facilities for firms. In particular, expanded unemployment insurance and furlough schemes have been adopted to stabilize the income of the workers, and contain the impact of the crisis on consumption and economic inequality. The concern is that a surge in inequality may erode social cohesion and spur support for populist or even undemocratic views.

Yet, how appropriate and effective these policies are remains unclear, mainly due to a lack of reliable indicators allowing to track economic activity at a fine temporal resolution. Indeed, most official statistics on inequality are available only at yearly frequency and often with long delays. This limits the ability of policymakers to rapidly adjust their responses in the effort to “flatten the recession curve” [ 1 ] after flattening the infection curve.

The COVID-19 has pushed new international initiatives to track economic activity in real time [ 2 – 6 ]. Researchers analyze the impact of economic stimulus packages to mitigate the effect of the COVID-19 epidemic on economic activity using high-frequency administrative data. Two examples are the effect on aggregate employment of the Paycheck Protection Program of the US [ 7 ] or the effect on consumption of the stimulus checks sent by the US Administration [ 8 ] using the data from financial aggregation and service apps [ 9 – 12 ].

One characteristic aspect of pandemics is their impact on inequality [ 13 , 14 ]. However, official inequality measures are calculated with long lags and low frequency. In the context of a fast-moving pandemic it is important to have a high-frequency measure of inequality to evaluate the mitigating effect of policy measures. This is particularly important in countries, like Spain, that suffered very intensively the financial crisis of 2008 and that have experienced an important increase in inequality since then. This process increased the support for populist parties, which in 2008 were not represented in the parliament and in 2020 accounted for 32.8% of the representatives in Congress. It is interesting to notice that inequality increased significantly from 2008 to 2012 but the process of growing political representation of populist parties happens mostly after 2013, even though inequality was decreasing since 2013. This seems to imply that there may be a threshold level of inequality that, once overcome, can trigger a set of popular grievances that persist over time, generating increasing support for populist parties. Therefore, a further increase in inequality, even in the short run, could imply reaching a level of inequality above the threshold that triggers future tension and political unrest. It could also ignite a process of increasing support for populist parties that could easily produce a significant deterioration of the institutional stability. Ultimately, this could have a long run effect on economic performance.

This paper uses bank account data and proposes a methodology to track the impact of government policies on inequality immediately after they are taken. Inequality is a multifaceted object and can concern dimensions as different as income, wealth, education etc. Our analysis focuses on wage inequality which, in countries with a high proportion of wage-earners, is a very precise indicator of overall income inequality (as we document for Spain). We do not look at wealth inequality mainly because, using information from just one financial institution, there is a high risk of not gauging a complete picture of the financial holdings of an individual. Bank account data have many advantages to study the effect of policy responses to the COVID-19 pandemic. They provide timely and reliable information on wages and government benefits. Being able to use very granular data, and to construct a high-frequency measure of inequality, allows to tailor policies to contain the increase of inequality in general, and by subgroups of the population classified by income level, gender, age, and county of birth.

Recent research has also used bank account data to study the evolution of different macro-magnitudes at very high frequency and, in particular, the effects of the pandemic on consumption [ 15 – 17 ]. Our contribution to this literature is threefold. First, and opposite to many papers in this literature [ 9 , 10 ], our sample is very representative of the population of Spanish wage-earners. As we show in next section, the distribution by gender and age are almost identical to the figures reported by the National Statistical Office. Second, and in contrast with a large part of the literature that uses banks accounts data, we are not analyzing the evolution of expenditure but the changes in the distribution of wages over time. Finally, the papers that have data on expenditure and income, like [ 16 ], deal with the issue of the sensitivity of consumption to income and do not consider the evolution of inequality, which is our basic objective.

We study empirically the evolution of inequality, before and after considering government support, comparing the period before the lockdown with the lockdown stage. We apply this methodology to data from a large Spanish bank. Spain is one of the countries most affected by the pandemic not only in terms of the number of people infected, but also regarding the economic impact. The comparison of the situation before and after the activation of the new policies of income support allows analyzing the effect of government interventions in the mitigation of inequality.

Using these bank account data, and our research design, we find that the largest impact of COVID-19 on inequality is transmitted through the movement of the distribution of salary changes among low wage earners. Second, we also find that most of the increase of inequality in the period after the beginning of the pandemic is mitigated by the action of the new extended unemployment benefits and furlough schemes activated by the government. There are no other changes in other government benefits during the period of analysis. We provide further details on the public income measures to support workers in the next section. Third we show that the policy response could not fully mitigate the large increase in inequality among young people and foreign-born individuals.

Materials and methods

We study the effect of COVID-19 on inequality using bank account data from CaixaBank, the second largest Spanish bank. Caixabank had monthly records on more than 3 million wage earners in 2020, and accounted for 27.1% of the wages, salaries and benefits deposited monthly in the Spanish financial sector. In Spain, differently from other countries like the US, the payment of the salaries or benefits using checks is a very rare event. Almost all the payments of salaries and benefits use direct deposits on bank accounts.

The wages and government benefits recorded by CaixaBank provide a large, precise and granular data source. Banks’ administrative data avoid most of the problems of surveys: there are no measurement errors or imperfect recollection mistakes, and they are obtained with short delays compared to surveys. For instance, the CaixaBank data provides the universe of wages through June 15, 2020 while the latest official measure of wage inequality in Spain, produced by the National Institute of Statistics, was published at the end of June of 2020, but referred to the situation in 2018.

The granularity of CaixaBank data allows also calculating inequality for subgroups of the population. Unlike other financial institutions, such as digital banks and personal finance management software, CaixaBank collects demographic information directly (gender, age, province, country of birth). We also provide a methodology to calculate monthly Gini indices and Lorentz curves, before and after accounting for public benefits, to analyze if the schemes to support workers temporarily out of the labor market are being effective at containing inequality.

The raw data are the wages and salaries deposited monthly at CaixaBank, and they present some challenges in order to construct wage inequality measures. We restrict our sample to accounts with either only one account holder or with multiple account co-holders but only one employer paying-in wages. This way, we ensure that payrolls or transfers recorded correspond to only one individual and avoid recording multiple payrolls or transfers from multiple account holders. In addition, we exclude from the sample those individuals who died during our period of study or who did not use the bank account for their usual financial transactions during the period. Finally, to ensure some stability on the sample of individuals studied, we require observing either wages or government benefits during two months (that is, in December 2019 and in January 2020) prior to the beginning of the period of study (February 2020). The S1 File explains all the details of our methodology to select the data.

Our reference sample includes individuals aged 16-64 who received either wages or unemployment benefits in December of 2019 and January of 2020. We follow those individuals in the months starting in February 2020. Since our main source of data is related with holding a bank account it is important to start analyzing the level of financial inclusion in Spain. The data of the Global Findex, the index of financial inclusion of the World Bank, shows that 97.6% of Spanish people over 15 years old holds a bank account when the average in high income countries is 93.7%.

We exclude the self-employed from our sample since it is difficult to calculate their net monthly income from bank account data: they receive payments from many different sources, and it is complicated to calculate expenses associated with their business. However, it is important to note that the proportion of wage earners among the Spanish working population was 84.4% in the first quarter of 2020 (Labor Force Survey of Spain, EPA). The relevance of wages as the main source of income can also be seen in the similarity of the inequality measures using income or gross wages. For instance, for the last period for which both measures are available, income inequality in Spain, measured by the Gini index, was 0.345 while wage inequality was 0.343.

Since most of the individuals in the sample are workers, to analyze its representativeness in terms of the distribution of wages we compare our data with the data of the latest Spanish National Statistical Office’s Wage Survey (Encuesta de Estructura Salarial, EES). For this purpose we consider the individuals in our sample who were working in February of 2020. First, we compare the distribution of individuals by gender and age with other sources. Table 1 summarizes the comparisons. In general, samples from digital banks and financial aggregation services have more young males than the general population. This is not the case with large and diversified traditional banks like our data source, CaixaBank. Table 1 shows that the gender and age distribution of our data is very similar to the working population.

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https://doi.org/10.1371/journal.pone.0249121.t001

In our sample, 54% of the individuals are male. This compares satisfactorily with the 52% of males in the sample of the last official survey (EES). In order to compare with more recent estimates, columns 3 and 4 include the proportions of males among employees in the Labor Force Survey of the last quarter of 2019 and first quarter of 2020. February of 2020 is between the last quarter of 2019 and the first quarter of 2020. The proportion of males is identical to the one in the EES and very close to the one in our sample. With respect to age, we also find that the proportions of workers in each age bracked in our sample are very similar to those reported in the EES and the EPA.

Fig 1 shows the distribution of the monthly wages of our sample compared with the distribution of monthly net salaries in the EES. The wages received by workers in their bank accounts are net of payroll taxes. In order to compare our data with the EES we have calculated the distribution of net salaries transforming the gross salaries of the EES into net salaries by subtracting social insurance payments and taxes withheld. The S1 File includes a detailed explanation of this transformation. Since there is a time difference between the last EES available and our data we have adjusted the wages by moving the whole distribution by the increase in the average wage since the last available EES. We can see that the histogram of the net wages of our sample is very well adjusted by the density estimation of the adjusted distribution of net salaries in the official wage survey. Both distributions are remarkably similar. The similarity of the distribution of wages, and also the characteristics of the workforce, confirms the representativeness of our sample.

https://doi.org/10.1371/journal.pone.0249121.g001

Since the distributions are so similar it is not surprising to see that the quantile ratios used regularly to describe inequality are very similar in both distributions as shown in S1 Table in S1 File .

Government support schemes for workers

The public policy response to mitigate the impact of the COVID-19 crisis in Spain has been large, as in most developed countries. For a detailed description of the economic impact of COVID-19 on the Spanish economy and the public policy reaction see [ 18 ]. The Spanish government has deployed income and liquidity support measures that are expected to reach 3.7% of GDP in discretionary measures and around 15.6% of GDP in off-budget measures [ 19 ].

Income measures to support workers have consisted mostly in the deployment of a furlough scheme (“Expediente de Regulación Temporal de Empleo”, or ERTEs) that was scarcely used until then. This scheme consists in a temporary job suspension (or a reduction in working hours) that avoids dismissals while maintaining the employment relationship. The Spanish government facilitated the use of this ERTE scheme due to COVID-19 (considering coronavirus as a force majeure, etc.) and increased coverage to all workers affected by a temporary job suspension. In addition, the benefits received did not reduce future unemployment benefit entitlements.

In addition to the job retention scheme, the government facilitated and extended the coverage of unemployment benefits. Regular unemployment benefits require a minimum of 360 days of contract employment in the previous 6 years and its duration is proportional to the amount of time worked (up to 18 months). Due to the pandemic, however, special unemployment subsidies were created for those who exhausted their unemployment benefits.

Those workers affected by job retention schemes and by unemployment received unemployment benefit transfers, which normally amounts to 70% of their social security contribution base. The schemes ensured an income stream during the duration of the contract suspension or unemployment, although of a lower amount than the regular salary.

Public transfers programs partially compensated wage losses for those workers that received them. However, despite the increase in coverage not all affected workers were entitled or had the same degree of coverage. In particular, those workers already unemployed before the pandemic or in temporary contracts that expired might not have had the right to unemployment benefits or only to reduced amounts. In addition, many of the beneficiaries experienced several months of delay before actually receiving their unemployment benefits in their bank accounts. All these developments might have affected the effectiveness of the government support to reduce inequality. This is of particular relevance in a country as Spain, which suffers from a very high labour market duality. In particular, subgroups of the population like young and foreign-born individuals are most likely to be in temporary contracts, and thus more heavily affected.

Large effect of the shutdown on pre-benefits inequality mostly due to low wage earners

To analyze the role of government benefits on inequality our analysis considers two scenarios: pre- and post-government benefits. In the pre-benefits scenario, we consider monthly wages before taking into account the benefits. The post-benefits scenario also considers unemployment insurance benefits, subsidies and furlough schemes.

Fig 2 shows the distribution of changes in pre-benefit wages between February and April 2020 (i.e., before vs. during the lockdown), represented by the solid lines. The x-axis reports the percentage change in wages experienced between the two months, while the y-axis reports the share of account holders in each category. The dashed lines represent the distribution for the same months of 2019, i.e., prior to the pandemic. The top left panel reports the distribution for the entire sample; the other panels report the distribution for each of five wage brackets (measured as of February of 2020): i) the interval between 900 to 1,000 euros, which includes the 25th percentile of the wage distribution; the interval between 1,200 to 1,300 euros, which includes the median; the interval between 1,700 to 1,800 euros, which includes the 75th percentile; the interval between 2,900 and 3,000 euros, which includes the 95 percentile, and the interval between 4,700 and 4,800, which represents the top 1%.

Pre-benefits scenario. Comparing 2020 and 2019.

https://doi.org/10.1371/journal.pone.0249121.g002

Several interesting facts emerge from Fig 2 . First, in 2020 the probability mass of the no-change interval is about half than in 2019. Compared to 2019, in 2020 a sizeable portion has moved to the no-income category. Furthermore, and most interestingly, in 2020 the probability of shifting to the no-income category is higher for individuals in the lower wage brackets.

Another noticeable aspect is that a substantial share of the highest wage earners experience a drastic wage reduction in April relative to February. This seasonal pattern, observed both in 2019 and 2020, is due to the payment of bonuses which occurs in February, and reaches over 30% for the top earners in our sample (i.e., the top 0.01%, not shown in the figure). There is no evidence of an analogous pattern for low wage earners.

S1 Fig in S1 File shows the difference in payments received by account holders between April and February after accounting for extended unemployment insurance and other benefits. Compared to the pre-benefits wages depicted in Fig 2 , the shift to the no-income category is much less pronounced. There is still a large wage reduction for high earners who are largely unaffected by government transfers.

S2 Fig in S1 File compares in the same graph all the levels of initial wages, before and after government benefits, to facilitate the comparisons.

To account for seasonality Fig 3 shows the difference in the proportion of changes in salaries between April and March of 2020 net of the the difference between the same two months in 2019. The S1 File shows the precise transformation to deal with seasonality. When seasonality is controlled for, the effect of the February bonuses for high wage earners disappears. Interestingly, the effect of the pandemic on pre-transfer earnings is very different for low and high wages. For wages below 1,300 euros the lower mass in the no-change brackets is associated with a corresponding shift to no-income category. The importance of the decline of employment for the lowest-income workers is common to other countries like the US [ 20 ]. For wages above 1,700 euros, instead, the lower mass in the no-change brackets is associated with a higher share of individuals experiencing small wage cuts. S3 Fig in S1 File shows the changes for all wage categories in the same figure.

April vs February—2020 vs 2019.

https://doi.org/10.1371/journal.pone.0249121.g003

To summarize the evolution of inequality we compute the Gini index. The S1 File presents a discussion of its calculation. Fig 4 Panel (a) depicts the evolution of the Gini index between February and May for 2020 and 2019, respectively. Both the pre- and post-benefits curves are basically parallel until April 2020, when the pre-benefits Gini index increases considerably while the post-benefit one only moderately. In May 2020 the pre-benefits Gini index remains very high, while the post-benefits index returns to the pre-pandemic level. From February until April of 2020 the pre-benefits Gini index increased close to 0.11 points. This implies a 25% increase in just two months. To evaluate the statistical significance of this large movement in the Gini index we can calculate the confidence intervals around our estimate. There are basically two possible procedures: using a Jakknife or a WLS estimator [ 21 ]. The S1 File describes the calculation of the standard error of the Gini index using a WLS estimator. As expected, given our large sample size, the standard error is very low (0.0002). This implies that the increase of 0.11 points observed in the Gini index between February and April of 2020 is highly statistically significant (well over the level of significance of 1%). Since the confidence intervals are tiny they cannot be visualized in the figures.

(a) Gini index (b) Theil index ( α = 1) (c) Lorentz curve: Pre-benefits, 2020 (d) Lorentz curve: Post-benefits, 2020.

https://doi.org/10.1371/journal.pone.0249121.g004

To confirm the robustness of the documented pattern to alternative measures of inequality, in Fig 4 Panel (b) we show the evolution of the Theil index, an inequality measure related to the concept of entropy and to Shannon’s index. The S1 File discusses the computation of this index. The Theil index shows a pattern very similar to the Gini index: a sizeable increase in March for both the pre- and post-benefits distribution which persists in April for the pre-benefit measure but not for the post-benefit one.

Panels (c) and (d) of Fig 4 show the changes in the pre- and post-benefits Lorenz curves respectively for every month between February and May 2020. It is apparent that, for the pre-benefits curve, the downward movement accelerates in April and stabilizes in May, while, for the post-benefit curve, the evolution is smoother.

Within group inequality post benefits has increased among young and foreign-born people

Given the granularity of the data we can also analyze the evolution of inequality within different subgroups of the population, differentiating by gender, age, and country of origin. Panel (a) in Fig 5 shows that there are not major differences in within inequality of males and females before the shock. The magnitude of the increase in the Gini index after the beginning of the pandemic is similar across genders before public transfers, but slightly higher for females in the post-benefits case.

(a) By gender. (b) By age group. (c) By place of birth.

https://doi.org/10.1371/journal.pone.0249121.g005

Panel (b) of Fig 5 shows the evolution of inequality for different age groups. For the youngest cohort (i.e., 16 to 29 years old), there is a considerable increase in the Gini index for pre-transfer earnings. The other groups also experience an increase in inequality though much smaller than for the young. The spike in the Gini index for the young is mitigated when considering the distribution of post-benefit earnings. Yet, the level of post-benefits inequality for this group is still remarkable, both in absolute and in relative terms. Such increase is arguably related to the fact that young workers account for a high proportion of temporary jobs in low wage occupations.

Panel (c) of Fig 5 shows the evolution of the Gini index separately for foreign-born individuals and for natives. As of January 1st 2020, foreign-born individuals represented 14.77% of the total Spanish resident population. Looking at the distribution of pre-benefits earnings, it is clear that inequality increases much more among foreign born than among natives. Such increase is less pronounced when looking at the post-benefits distribution, though, in this case as well, the Gini index for foreign born is significantly higher than for natives.

Interesting differences emerge when dividing foreign-born individuals by the per capita GDP level of the country of origin. For example, as shown in S4 Fig in S1 File , while post-benefits inequality decreases over time for both natives and foreign-born from high-income countries, it remains high for foreign-born from low-income countries.

The disproportionate increase in post-benefit inequality among poorer migrants attests to their vulnerability in times of crisis as their social welfare net is thinner. Foreign born workers from low income countries tend to have occupations with low salaries, and a high proportion of temporary jobs. In many cases they work without a formal contract which means that they cannot prove they were working before the pandemic and, therefore, they cannot get the benefits that other workers get. On the other hand expatriates from high income countries still enjoy a high salary.

Finally, inequality increases more in regions that rely heavily on tourism (e.g., Balearic and Canary Islands) than in other parts of the country (S5 Fig in S1 File ). This is not surprising since the touristic sector is characterized by a high proportion of low wage workers who, as shown above, are the ones most affected by the job losses and wage cuts caused by the pandemic.

The financial crisis of 2008 generated a large increase in inequality in many countries. When some countries were still trying to recover from the financial crisis a new shock, the COVID-19, has hit the economy. Recent research shows that social distancing laws are not responsible for the economic harm [ 15 ] and the responses to emergency declarations are strongly differentiated by income [ 22 ]. In this paper we show that the economic impact is also very heterogeneous by income level which, in turn, is reflected in large increases in inequality before governments policy response.

Our findings contribute to a recent literature on the measurement of economic indicators in real-time, or at very high frequency. Most of the economic research on the impact of COVID-19 has concentrated on its effect on consumption [ 6 , 8 , 15 – 17 ]. We present evidence on the impact of COVID-19 on economic inequality. Our findings show that, before accounting for extended unemployment insurance and furlough benefits, the economic impact of the pandemic caused a large increase of inequality. After considering public benefits the effect of the crisis on inequality is mitigated. We show how bank account data of a representative financial institution can be used to track inequality and monitor the effect of economic polity on its evolution. In contrast with some previous research that uses data on personal finance websites and bank accounts, our data replicates very precisely the distribution of the population of wage earners.

We present evidence that shows a very heterogeneous impact of the pandemic on inequality by income level, age and country of birth of the individuals. Our methodology could be applied to many other countries that have introduced income-support schemes similar to the ones considered in Spain (furlough benefits and extended unemployment insurance). Tracking, at high frequency, the effect of policy responses on inequality allows tuning the policy instruments to mitigate inequality, targeting the groups that contribute the most to the increase of inequality.

Supporting information

https://doi.org/10.1371/journal.pone.0249121.s001

Acknowledgments

We want to thank Miguel Angel Barcia for his helpful suggestions. Daniele Alimonti provided excellent research assistance.

• 1. Gourinchas PO. “Flattening the Pandemic and Recession Curves” in Mitigating the COVID Economic Crisis: Act Fast and Do Whatever, Ed. Baldwin R., Weder di Mauro B. (Chapter 2, 31–40). CEPR Press, London, UK), 2020.
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• 17. Bachas N., Ganong P., Noel P., Vara J., Wong A., Farrell D., et al. Initial impact of the pandemic on consumer behavior: evidence from linked income, spending, and savings data. NBER Working Paper 27617, 2020.
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• Published: 30 January 2018

What has economics got to do with it? The impact of socioeconomic factors on mental health and the case for collective action

• Anna Macintyre 1 ,
• Daniel Ferris 2 ,
• Briana Gonçalves 3 &
• Neil Quinn 1  

Palgrave Communications volume  4 , Article number:  10 ( 2018 ) Cite this article

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A clear link exists between social and economic inequality and poor mental health. There is a social gradient in mental health, and higher levels of income inequality are linked to higher prevalence of mental illness. Despite this, in the late 20th and early 21st century, psychiatric and psychological perspectives have dominated mental health research and policy, obscuring root socioeconomic contributors. Drawing on contemporary research on the social determinants of mental health, with particular reference to Europe and the U.S., this paper argues that a sharper focus on socioeconomic factors is required in research and policy to address inequalities in mental health. Current attempts to move this direction include: evaluation of the impact of economic policies on mental health, community-based partnerships, increased professional awareness and advocacy on socioeconomic factors. This necessitates greater understanding of the barriers to such actions. This paper argues that advancing ‘upstream’ approaches to population mental health requires an interdisciplinary research vision that supports greater understanding of the role of socioeconomic factors. It also demands collective cross-sectoral action through changes in social and economic policy, as well as economic frameworks that move beyond an exclusive focus on economic growth to embrace collective and societal wellbeing.

The importance of socioeconomic factors for mental health

'Economics is the mother tongue of public policy, the language of public life, and the mindset that shapes society' (Raworth, 2017 , p. 6)

Growing evidence connects economic inequality and poor mental health (Friedli, 2009 ; Pickett and Wilkinson, 2010 ; Platt et al., 2017 ). Experience of socioeconomic disadvantage, including unemployment, low income, poverty, debt and poor housing, is consistently associated with poorer mental health (Silva et al., 2016 ; Elliott, 2016 ; Platt et al., 2017 ; Friedli, 2009 , Rogers and Pilgrim, 2010 ). Mental health problems are particularly prominent amongst marginalised groups experiencing social exclusion, discrimination and trauma, leading to compound vulnerability (Rafferty et al., 2015 ). Greater inequality within societies is associated with greater prevalence of mental illness (Wilkinson and Pickett, 2009 ; Pickett and Wilkinson, 2010 ), and economic recessions have had devastating impacts on population mental health (Platt et al., 2017 ; Wahlbeck and McDaid, 2012 ). At a global level, mental health and substance use disorders account for between one fifth and almost one third of Years Lived with Disability (Whiteford et al., 2013 ; Vigo et al., 2016 ). At the same time there is increasing interest in how to promote positive mental health at a societal level (Friedli, 2009 ; Rogers and Pilgrim, 2010 ; Hanlon and Carlisle, 2013 ).

However, the dominance of medical, psychiatric and psychological perspectives on mental health from the 1970s onwards has distracted from socioeconomic factors (Smith, 2016b ; Shim et al., 2014 ). Drawing on contemporary research on the social determinants of mental health, with particular reference to Europe and the U.S., this paper argues that a sharper focus on socioeconomic factors is required in research and policy to address inequalities in mental health.

Contemporary research on socioeconomic determinants of mental health

'Today, in the wake of the global economic slowdown, rising rates of mental illness and disaffection with psychopharmacology, the idea that there are social determinants of mental health is taking root once more'. (Smith, 2016b , p. 9)

There is growing interest across disciplines in understanding and addressing the social determinants of mental health (Friedli, 2009 ; Fisher and Baum, 2010 ; Bowen and Walton, 2015 ; Kinderman, 2016 ; Compton and Shim, 2015 ; Smith, 2016b ; Silva et al., 2016 ). This sits alongside increased attention to public mental health, and the promotion of positive societal well-being (Wahlbeck, 2015 ; Rogers and Pilgrim, 2010 ; Hanlon and Carlisle, 2013 ). The role of psychosocial factors and chronic stress has also been emphasised in understanding health inequalities (Fisher and Baum, 2010 ; Wilkinson and Pickett, 2017 ). Furthermore, stigma (a ubiquitous component of mental health difficulties), has been recognised as a fundamental cause of health inequalities (Hatzenbuehler et al., 2013 ).

However, within the broad literature on the social determinants of mental health, to what extent are socio-economic factors considered? There is consistent evidence supporting the link between socioeconomic inequality in terms of income, employment, and neighbourhood environments and poorer mental health outcomes (Silva et al., 2016 ). At an ecological level, a significant relationship has been shown between higher income inequality (as measured by the Gini coefficient) and higher incidence rates of schizophrenia (Burns et al., 2014 ). In addition, the connection between experience of socioeconomic disadvantage and increased risk of suicidal behaviour has also been established (Platt et al., 2017 ). Furthermore, the association between educational inequalities and mental health outcomes may be attenuated by controlling for employment status, indicating the importance of employment for mental health (Katikireddi et al., 2016 ). At a community level, low socioeconomic status may lead to greater concerns about neighbourhood safety, and decrease the amount of physical activity in the community, with consequent impacts on mental health (Meyer et al., 2014 ). A focus on socioeconomic factors may also link with ideas of social capital or community efficacy, measures of trust and commitment by residents to a neighbourhood (Platt et al., 2017 ), which have been linked to rates of depression, suicide, and internalising behaviours (Schmidt et al., 2014 ).

Many argue for a renewed focus on social justice, advocating for the significance of socioeconomic factors for mental health (Friedli, 2009 ; Rogers and Pilgrim, 2010 ). The impact of material and economic conditions and consumerism on population wellbeing is also recognised (Rogers and Pilgrim, 2010 ; Friedli, 2009 ). From a U.S. perspective, Jones et al. ( 2009 ) offer a theoretical framework to identify the social determinants of inequity shaped by systems of power and the distribution of resources, including an economic system that creates class structures and dimensions of opportunity (Jones et al., 2009 ). In addition, disparities in education and income play a major role in understanding racial difference in health and mental health (Williams et al., 1997 ). Krieger et al. ( 1997 ) argue that social class, at the household and community level, predicts inequalities in health (Krieger et al., 1997 ), and the role of economic inequality, poverty, and deprivation is implicated in poor mental health in the United States (Compton and Shim, 2015 ; Manseau, 2015 ).

Despite this, in comparison with biomedical, neuropsychiatric and psychological literature, the social determinants of mental health are strikingly understudied (Shim et al., 2014 ). In Europe, research on the prevention of poor mental health has received a comparatively low level of investment (Wykes et al., 2015 ). In the United States, funding of prevention constitutes a notoriously small percentage of overall healthcare expenditures (Miller et al., 2012 ). Yet the economic cost of treatment and lost productivity related to mental health and substance use disorder is well documented. While the National Institute for Mental Health named prevention as a core objective in its strategic plan for research (National Institute for Mental Health, 2015 ), there is not a clear picture of the scope and scale of investment in mental health prevention across government and philanthropy. It is likely there has been even less investment in research on the social determinants of mental health, and socioeconomic factors in particular. Thus, there is a need for greater research capacity (Wahlbeck and McDaid, 2012 ).

Moving from evidence to action: policy, communities and practice

'levels of mental distress among communities need to be understood less in terms of individual pathology and more as a response to relative deprivation and social injustice' (Friedli, 2009 , p.III)

However, it is not only further evidence on the link between economic inequality and mental health that is required, but also action to address it (Smith, 2016b ). This may require a shift from addressing individuals’ psychological states to a focus on social justice and broader economic conditions. Current attempts to move this direction include action in policy, communities and service provision.

In policy, this agenda was advanced by a World Health Organisation report in 2014, which highlighted the social determinants of mental health at an international level (World Health Organization, 2014 ). In Europe, the Joint Action on Mental Health has championed a focus on ‘Mental Health in All Policies’, which promotes action in non-health policy areas including employment and welfare (EU Directorate General for Health and Food Safety, 2015 ). Evidence is beginning to accumulate on relevant policy actions, including labour market regulation (Katikireddi et al., 2016 ) and part-time sickness absence (EU Directorate General for Health and Food Safety, 2015 ), investment in social protection (Niedzwiedz et al., 2016 ), and protective employment policies (Platt et al., 2017 ). In the United States, better population health outcomes have also been found in states with more progressive policies such as minimum wage and corporate tax rates (Rigby and Hatch, 2016 ). It has also been raised that a Universal Basic Income might positively impact on population mental health (Smith, 2016b ). Whilst there is evidence for interventions which can lessen the impact of poverty and inequality on mental health, including interventions aimed at the individual or family level (e.g., parenting interventions), evidence is more limited on community interventions or on cross-sectoral action on policies (Wahlbeck et al., 2017 ).

At a community level, the expansion of the Community Schools model in the U.S., which provides children in socioeconomically disadvantaged areas, with access to health services (medical, dental, vision and counselling services), brings more holistic attention to the education and healthy development of children (Oakes and Daniel, 2017 ). Education policies that recognise structural inequalities show promise to close the economic and achievement gap. Additionally, New York City has launched Thrive NYC, a comprehensive city-based mental health plan to reduce stigma, intervene early, and improve access to services (NYC Thrive, 2016 ). Encouraging partnership and reducing silos, a major component of the initiative, has linked community based organisations serving the most socially and economically disadvantaged populations with mental health providers to increase access to mental health and substance use services (Chapman et al., 2017 ). Furthermore, efforts at a community level which promote social capital are promoted as a buffer against the impact of socioeconomic factors (Wahlbeck and McDaid, 2012 ).

At the level of service provision, there are moves to increase professional awareness and advocacy on the social determinants of mental health (Compton and Shim, 2015 , Shim et al., 2014 ). This may include a focus on social justice and socioeconomic factors in therapeutic work. Kinderman argues 'practical help to resolve real-world issues such as debt, employment issues, housing problems and domestic violence' may be important roles for clinicians (Kinderman, 2016 , p. 4). Shim et al. ( 2014 ) also suggest that mental health professionals have an advocacy role to influence public policies that impact on mental health (Shim et al., 2014 ). Bowen and Walton argue that there is a role for social workers in addressing racial and ethnic disparities in mental health (Bowen and Walton, 2015 ). One relevant example from the U.K. is the work of Psychologists Against Austerity, who have campaigned on the mental health impact of welfare policies (McGrath et al., 2016 ).

Trying to focus ‘upstream’: barriers to action on socioeconomic factors

'We are failing on health equity because we are failing on equity' (Braveman, 2012 , p. 515)

A distinction is often made between 'upstream societal influences' (which can include living and working conditions and wider societal structures) and 'downstream risk factors' (which include behaviours such as smoking or drinking as well as biological risk factors) (Graham, 2009 , p. 472). To effectively take action on socioeconomic factors and mental health, there is a need for awareness of what might pull research and policy ‘downstream’ (Douglas, 2016 ; Graham, 2009 ). These barriers might include the dominance of the current economic paradigm, a focus on psychological or community resilience, ignoring factors like structural racism, or the challenges of mental health care provision.

In health inequalities research it is argued that an exclusive focus on health may over-medicalise the issue, veiling the fundamental problem of social inequality (Lynch, 2017 ; Douglas, 2016 ). It is stated that efforts should include awareness of the socioeconomic and political contexts which generate health inequalities, particularly the influence of neoliberalism (Smith et al., 2016a ; Collins et al., 2016 ) Such arguments are equally salient to mental health. However, focusing ‘upstream’ presents challenges given that the dominant neoliberal paradigm 'actively embraces inequality' (Collins et al., 2016 , p. 129). This may point to confronting the current inequitable economic paradigm and considering alternatives to economic growth that incorporate broader social and environmental concerns (Fioramonti, 2016 ; Raworth, 2017 ). A sharper focus on fundamental inequalities, and the economic system which underpins them, may be critical to addressing the ‘upstream’ influences on mental health.

It has also been argued that it may be problematic to focus on psychological or community assets and strengths, and social capital, as this may mask a focus on socioeconomic factors, which are fundamental causes of distress (Friedli, 2016; Rogers and Pilgrim, 2010 ; Knifton, 2015 ). Indeed, Friedli argues: 'Choosing psycho -analysis over economic analysis has serious consequences for how public health explains and responds to issues of social justice' (Friedli, 2016 , p. 216, original emphasis). This argument may be particularly relevant for mental health, where psychological conceptualisations may predominate. Within a neoliberal policy framework, there is the danger of endorsing individualistic conceptualisations of complex social and economic problems, where the predominant biomedical model has often resulted in a systematic neglect of the impact of social and structural barriers experienced by people with poor mental health (Bayetti et al., 2016 ; Friedli, 2016 ). Thus, whilst the relevance of psychosocial factors is recognised, it is important to increase the salience of social and economic inequalities which generate inequalities in mental health at a population level.

Furthermore, it is critical to consider race and ethnicity (Lynch and Perera, 2017 ). While racism has been identified as a social determinant of health, there is a significant lack of research or policy to address it (Bailey et al., 2017 ; Rafferty et al., 2015 ). Advancing policies to tackle structural racism may have significant implications for population mental health. Despite having distinct healthcare systems and ideologies on healthcare access, both the U.S. and U.K. have significant health inequalities by race and ethnicity (Bailey et al., 2017 ; Department of Health, 2009 ). Research on mental health and racial discrimination has largely considered interpersonal discrimination, not structural racism and the link to inequalities (Bailey et al., 2017 ). While increased funding and resources for mental health services and prevention is needed, greater attention must be given to addressing structural racism that leads to inequalities in education, employment, and mental health.

Finally, the need to ensure adequate mental health care provision is a pressing concern in both Europe and the U.S. Indeed, many OECD countries face ongoing challenges regarding adequate levels of resourcing for mental health services (Wahlbeck and McDaid, 2012 ). Current healthcare policy debates in the U.S. threaten progress in increasing the number of insured individuals as well as what services they can receive. Current debate, focused on insurance access and eligibility, is troublingly void of a focus on prevention or addressing social determinants and structural racism. In fact, while mental health care access improved following implementation of the Affordable Care Act, there was no progress in reducing racial and ethnic disparities (Creedon and Le Cook, 2016 ). While advocates and researchers are pulled toward policy and legislative fights over healthcare provision, larger macro issues impacting health and mental health, i.e. social determinants, are lost. Negotiating space for dialogue on the importance of prevention, alongside service provision, will be crucial.

Conclusions: taking collective action

Smith ( 2016b ) argues that a focus on socioeconomic factors and mental health is not new, but had previously gained ground in the early 20th century (Smith, 2016b ). As a renewed interest emerges in the current context, there are increasing calls for collective actions (Kinderman, 2016 ) and inter-disciplinary and inter-sectoral approaches, which re-invigorate a focus on fundamental socioeconomic inequalities and social justice (Friedli, 2009 ; Braveman, 2012 ).

Encouragingly, the growing body of research on socioeconomic factors and social determinants of health is narrowing in on mental health. Diagnosing problems, however, is not enough. Evidence on policy actions and a collective appreciation of issues that prevent upstream approaches is also needed: structural barriers including racism and discrimination, the medicalising of population mental health, access and quality of services, and ultimately the economic system itself.

To advance upstream approaches will require an inter-disciplinary research vision which extends beyond biomedical, neuropsychiatric and psychological models of mental health, and which supports greater understanding of the role of socioeconomic factors and economics. It will necessitate bold cross-sectoral policy action including changes to wider social and economic policies such as social protection, taxation, employment and housing policy, as well as health policy. Given the ubiquity and influence of economics, this agenda should be supported by the advancement of paradigms that move beyond an exclusive focus on economic growth (Raworth, 2017 ; Fioramonti, 2016 ), and which appreciate the importance of collective and societal wellbeing (Knifton, 2015 ).

Population mental health is intimately connected to societal economic conditions. The (poor) mental health of modern societies offers a stark indication of the consequences of not taking action: 'economic growth at the cost of social recession' (Friedli, 2009 , p. IV). Socioeconomic inequality may be 'the enemy between us' (Wilkinson and Pickett, 2017 , p. 11), increasing status competition, undermining the quality of social relations, increasing stress and impacting on health, mental health, and wellbeing. In response to this, there is a need to build an economic system that tackles these inequalities in mental health.

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The collaboration for this paper was made possible by a European Union funded Horizon 2020 RISE project ‘Citizenship, Recovery and Inclusive Society Partnership’ ( www.crisppartnership.eu) . This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement, No 690954. The views in this publication are solely the responsibility of the authors. The Commission is not responsible for any use that may be made of the information it contains.

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Macintyre, A., Ferris, D., Gonçalves, B. et al. What has economics got to do with it? The impact of socioeconomic factors on mental health and the case for collective action. Palgrave Commun 4 , 10 (2018). https://doi.org/10.1057/s41599-018-0063-2

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Is economic inequality really a problem a review of the arguments.

1. Introduction

2. some basic facts about inequality.

But from the moment one man needed the help of another, as soon as it was thought to be useful for a single person to have provisions for two, equality disappeared, property was introduced, labor became necessary, and vast forests were changed into smiling fields which had to be watered with the sweat of men, and in which slavery and misery were soon seen to germinate and grow with the crops.... Metallurgy and agriculture were the two arts whose invention produced this great revolution. ( Rousseau [1753] 2011, p. 76 )

3. Arguments that Economic Inequality Is Not a Threat to Social Justice or Economic Stability

4. arguments that inequality in itself is a grave social problem, 4.1. the intrinsic value of greater equality: distributive justice.

The economic framework that each society has—its laws, institutions, policies, etc.—results in different distributions of economic benefits and burdens across members of the society. These economic frameworks are the result of human political processes and they constantly change both across societies and within societies over time... Arguments about which frameworks and/or resulting distributions are morally preferable constitute the topic of distributive justice.

4.2. The Instrumental Value of Reducing Inequality

4.2.1. the economic effects of inequality, 4.2.2. inequality, politics, and democracy, 4.2.3. behavioral changes and health disparities, 4.2.4. inequality and social and environmental ills.

The rich are disproportionate contributors to the carbon emissions that power climate change. It is cruel and perverse, therefore, that the costs of warming should be disproportionately borne by the poor. And it is both insult and injury that the wealthy are more mobile in the face of climate-induced hardship, and more effective at limiting the mobility of others. The strains this injustice places on the social fabric might well lead to woes more damaging than rising temperatures themselves. (p. 66)

5. Conclusions

Conflicts of interest.

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Peterson, E.W.F. Is Economic Inequality Really a Problem? A Review of the Arguments. Soc. Sci. 2017 , 6 , 147. https://doi.org/10.3390/socsci6040147

Peterson EWF. Is Economic Inequality Really a Problem? A Review of the Arguments. Social Sciences . 2017; 6(4):147. https://doi.org/10.3390/socsci6040147

Peterson, E. Wesley F. 2017. "Is Economic Inequality Really a Problem? A Review of the Arguments" Social Sciences 6, no. 4: 147. https://doi.org/10.3390/socsci6040147

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