research paper about economic inequality

• Search Menu
• Author Guidelines
• Submission Site
• Open Access
• About International Studies Review
• About the International Studies Association
• Editorial Board
• Advertising and Corporate Services
• Journals Career Network
• Self-Archiving Policy
• Dispatch Dates
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

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

• < Previous

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

• Article contents
• Figures & tables
• Supplementary Data

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

• Permissions Icon Permissions

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.

Acemoglu Daron , Robinson James A. . 2006 . Economic Origins of Dictatorship and Democracy . Cambridge, MA : Cambridge University Press .

Google Scholar

Google Preview

Aghion Philippe , Caroli Eve , García-Peñalosa Cecilia . 1999 . “ Inequality and Economic Growth: The Perspective of the New Growth Theories .” Journal of Economic Literature 37 ( 4 ): 1615 – 60 .

Aiyar Shekhar , Ebeke Christian . 2020 . “ Inequality of Opportunity, Inequality of Income and Economic Growth .” World Development 136 : 105115 .

Alesina Alberto , Rodrik Dani . 1994 . “ Distributive Politics and Economic Growth .” The Quarterly Journal of Economics 109 ( 2 ): 465 – 90 .

Alesina Alberto , Perotti Roberto . 1996 . “ Income Distribution, Political Instability, and Investment .” European Economic Review 40 ( 6 ): 1203 – 28 .

Andersen Robert . 2012 . “ Support for Democracy in Cross-National Perspective: The Detrimental Effect of Economic Inequality .” Research in Social Stratification and Mobility 30 ( 4 ): 389 – 402 .

Ansell Ben , Samuels David . 2010 . “ Inequality and Democratization: A Contractarian Approach .” Comparative Political Studies 43 ( 12 ): 1543 – 74 .

Atkinson Anthony B. , Brandolini Andrea . 2001 . “ Promise and Pitfalls in the Use of ‘Secondary’ Data-Sets: Income Inequality in OECD Countries as a Case Study .” Journal of Economic Literature 39 ( 3 ): 771 – 99 .

Atkinson Anthony B. , Brandolini Andrea . 2004 . “ Global World Inequality: Absolute, Relative or Intermediate? ” Paper prepared for the 28th General Conference of The International Association for Research in Income and Wealth, Cork, Ireland, August 22–28, 2004 .

Atkinson Anthony B. , Brandolini Andrea . 2009 . “ On Data: A Case Study of the Evolution of Income Inequality across Time and across Countries .” Cambridge Journal of Economics 33 ( 3 ): 381 – 404 .

Bandiera Oriana , Natraj Ashwini . 2013 . “ Does Gender Inequality Hinder Development and Economic Growth? Evidence and Policy Implications .” The World Bank Research Observer 28 ( 1 ): 2 – 21 .

Banerjee Abhijit V. , Duflo Esther . 2003 . “ Inequality and Growth: What Can the Data Say? ” Journal of Economic Growth 8 ( 3 ): 267 – 99 .

Barro Robert J. 1999 . “ Determinants of Democracy .” Journal of Political Economy 107 ( S6 ): S158 – 83 .

Barro Robert J. . 2000 . “ Inequality and Growth in a Panel of Countries .” Journal of Economic Growth 5 : 5 – 32 .

Barro Robert J. . 2008 . “ Inequality and Growth Revisited .” ADB Working Paper Series on Regional Economic Integration 11. Asian Development Bank (ADB), Philippines. Accessed November 16, 2020. https://www.adb.org/sites/default/files/publication/28468/wp11-inequality-growth-revisited.pdf .

Bénabou Roland . 1996 . “ Inequality and Growth .” In NBER Macroeconomics Annual 1996 , Volume 11, edited by Bernanke Ben S. , Rotemberg Julio J. , 11 – 92 . Cambridge, MA : MIT Press .

Bénabou Roland . 2000 . “ Unequal Societies: Income Distribution and the Social Contract .” American Economic Review 90 ( 1 ): 96 – 129 .

Berg Andrew , Ostry Jonathan D. , Sangarides Charalambs G. , Yakhshilikov Yorbol . 2018 . “ Redistribution, Inequality, and Growth: New Evidence .” Journal of Economic Growth 23 ( 4 ): 259 – 305 .

Bermeo Nancy . 2009 . “ Poverty Inequality and Democracy (II): Does Electoral Democracy Boost Economic Equality? ” Journal of Democracy 20 ( 4 ): 21 – 35 .

Birdsall Nancy . 1999 . “ Education: The People's Asset .” Center on Social and Economic Dynamics Working Paper 5. Accessed August 13, 2020. https://www.researchgate.net/publication/5059764_Education_The_People's_Asset .

Birdsall Nancy , Londoño Juan Luis . 1997 . “ Asset Inequality Matters: An Assessment of the World Bank's Approach to Poverty Reduction .” The American Economic Review 87 ( 2 ): 32 – 37 .

Blotevogel Robert , Imamoglu Eslem , Moriyama Kenji , Sarr Babacar . 2020 . “ Measuring Income Inequality and Implications for Economic Transmission Channels .” IMF Working Paper WP/20/164 . Washington, DC : International Monetary Fund .

Boix Carles . 2003 . Democracy and Redistribution . Cambridge : Cambridge University Press .

Boix Carles , Stokes Susan C. . 2003 . “ Endogenous Democratization .” World Politics 55 ( 4 ): 517 – 49 .

Bollen Kenneth A. , Jackman Robert W. . 1985 . “ Political Democracy and the Size Distribution of Income .” American Sociological Review 50 ( 4 ): 438 – 57 .

Bosmans Kristof , Decanq Koen , Decoster André . 2014 . “ The Relativity of Decreasing Inequality between Countries .” Economica 81 ( 322 ): 276 – 92 .

Bourguignon Francois . 2015 . “ Revisiting the Debate on Inequality and Economic Development .” Revue d'économie politique 5 : 633 – 63 .

Buchmann Claudia , Hannum Emily . 2001 . “ Education and Stratification in Developing Countries: A Review of Theories and Research .” Annual Review of Sociology 27 ( 1 ): 77 – 102 .

Cancela Joao , Geys Benny . 2016 . “ Explaining Voter Turnout: A Meta-Analysis of National and Subnational Elections .” Electoral Studies 42 : 264 – 75 .

Castelló-Climent Amparo. 2010 . “ Inequality and Growth in Advanced Economies: An Empirical Investigation .” The Journal of Economic Inequality 8 : 293 – 321 .

Castells-Quintana David , Royuela Vicente , Thiel Fabian . 2019 . “ Inequality and Sustainable Development: Insights from an Analysis of the Human Development Index .” Sustainable Development 27 ( 3 ): 448 – 60 .

Checchi Daniele 2003 . “ Inequality in Incomes and Access to Education: A Cross-Country Analysis (1960–95) .” Labour 17 ( 2 ): 153 – 201 .

Chong Alberto , Gradstein Mark . 2007 . “ Inequality and Institutions .” Review of Economics and Statistics 89 ( 3 ): 454 – 65 .

Chong Alberto , Gradstein Mark . 2019 . “ Institutional Persistence, Income Inequality, and Individual Attitudes .” The Journal of Economic Inequality 17 ( 3 ): 401 – 13 .

Cingano Federico . 2014 . “ Trends in Income Inequality and Its Impact on Economic Growth .” OECD Social, Employment and Migration Working Papers 163 . Paris : OECD Publishing .

Clarke George R.G. 1995 . “ More Evidence on Income Distribution and Growth .” Journal of Development Economics 47 ( 2 ): 403 – 27 .

Dabla-Norris Era , Kochhar Kalpana , Suphaphiphat Nujin , Ricka Frantisek , Tsounta Evridiki . 2015 . “ Causes and Consequences of Income Inequality: A Global Perspective .” IMF Discussion Note SDN/15/13 . Washington, DC : International Monetary Fund .

De La Croix David , Doepke Matthias . 2003 . “ Inequality and Growth: Why Differential Fertility Matters .” American Economic Review 93 ( 4 ): 1091 – 113 .

Deaton Angus 2003 . “ Health, Inequality, and Economic Development .” Journal of Economic Literature 41 ( 1 ): 113 – 58 .

Deininger Klaus , Olinto Pedro . 2000 . “ Asset Distribution, Inequality, and Growth .” Policy Research Working Paper 2375 . Washington, DC : The World Bank .

Deininger Klaus , Squire Lyn . 1996 . “ A New Data Set Measuring Income Inequality .” The World Bank Economic Review 10 ( 3 ): 565 – 91 .

Deininger Klaus , Squire Lyn . 1998 . “ New Ways of Looking at Old Issues: Inequality and Growth .” Journal of Development Economics 57 ( 2 ): 259 – 87 .

Dorsch Michael T. , Maarek Paul . 2020 . “ Economic Downturns, Inequality, and Democratic Improvements .” European Journal of Political Economy 62 ( C ): 1 – 21 .

Easterly William . 2007 . “ Inequality Does Cause Underdevelopment: Insights from a New Instrument .” Journal of Development Economics 84 ( 2 ): 755 – 76 .

Ehrhart Christophe . 2009 . “ The Effects of Inequality on Growth: A Survey of the Theoretical and Empirical Literature .” ECINEQ Working Paper Series 2009-107. ECINEQ - Society for the Study of Economic Inequality. Accessed November 16, 2020. http://www.ecineq.org/milano/WP/ECINEQ2009-107.pdf .

El-Shagi Makram , Shao Liang . 2019 . “ The Impact of Inequality and Redistribution on Growth .” Review of Income and Wealth 65 ( 2 ): 239 – 63 .

Esposito Lucio , Villaseñor Adrián . 2018 . “ Wealth Inequality, Educational Environment and School Enrolment: Evidence from Mexico .” The Journal of Development Studies 54 ( 11 ): 2095 – 118 .

Ferreira Francisco H.G. , Lakner Christoph , Lugo Maria Ana , Özler Berk . 2018 . “ Inequality of Opportunity and Economic Growth: How Much Can Cross-Country Regressions Really Tell Us? ” Review of Income and Wealth 64 ( 4 ): 800 – 27 .

Ferreira Francisco H.G. , Lustig Nora , Teles Daniel . 2015 . “ Appraising Cross-National Income Inequality Databases: An Introduction .” The Journal of Economic Inequality 13 : 497 – 526 .

Fields Gary . 1989 . “ A Compendium of Data on Inequality and Poverty for the Developing World .” Unpublished manuscript, Cornell University .

Flug Karnit , Spilimbergo Antonio , Wachtenheim Erik . 1998 . “ Investment in Education: Do Economic Volatility and Credit Constraints Matter? ” Journal of Development Economics 55 ( 2 ): 465 – 81 .

Forbes Kristin J. 2000 . “ A Reassessment of the Relationship between Inequality and Growth .” American Economic Review 90 ( 4 ): 869 – 87 .

Fukuyama Francis . 2011 . “ Poverty, Inequality, and Democracy: Dealing with Inequality .” Journal of Democracy 22 ( 3 ): 79 – 89 .

Galor Oded , Zeira Joseph . 1993 . “ Income Distribution and Macroeconomics .” The Review of Economic Studies 60 ( 1 ): 35 – 52 .

Galor Oded , Moav Omer . 2004 . “ From Physical to Human Capital Accumulation: Inequality and the Process of Development .” Review of Economic Studies 71 ( 4 ): 1001 – 26 .

García-Peñalosa Cecilia . 1995 . “ The Paradox of Education or the Good Side of Inequality .” Oxford Economic Papers 47 ( 2 ): 265 – 85 .

Glaeser Edward , Scheinkman Jose , Shleifer Andrei . 2003 . “ The Injustice of Inequality .” Journal of Monetary Economics 50 ( 1 ): 199 – 222 .

Graham Carol . 2014 . “ Concluding Remarks. How Inequality Matters to Well-Being .” In Happiness and Economic Growth: Lessons from Developing Countries , edited by Clark Andrew E. , Senik Claudia , 249 – 66 . Oxford : Oxford University Press .

Gründler Klaus , Scheuermeyer Philipp . 2018 . “ Growth Effects of Inequality and Redistribution: What Are the Transmission Channels? ” Journal of Macroeconomics 55 : 293 – 313 .

Gutiérrez Catalina , Tanaka Ryuichi . 2009 . “ Inequality and Education Decisions in Developing Countries .” The Journal of Economic Inequality 7 : 55 – 81 .

Haggard Stephan , Kaufman Robert R. . 2012 . “ Inequality and Regime Change: Democratic Transitions and the Stability of Democratic Rule .” American Political Science Review 106 ( 3 ): 495 – 516 .

Halter Daniel , Oechslin Manuel , Zweimüller Josef . 2014 . “ Inequality and Growth: The Neglected Time Dimension .” Journal of Economic Growth 19 ( 1 ): 81 – 104 .

Houle Christian 2009 . “ Inequality and Democracy: Why Inequality Harms Consolidation But Does Not Affect Democratization .” World Politics 61 ( 4 ): 589 – 622 .

Houle Christian . 2015 . “ Does Inequality Harm Economic Development and Democracy?: Accounting for Missing Values, Noncomparable Observations, and Endogeneity .” In The Oxford Handbook of the Politics of Development , edited by Lancaster Carol , Walle Nicolas Van de , 140 – 61 . New York : Oxford University Press .

Houle Christian . 2018 . “ Does Economic Inequality Breed Political Inequality? ” Democratization 25 ( 8 ): 1500 – 18 .

Islam Md. Rabiuk , McGillivray Mark . 2020 . “ Wealth Inequality, Governance and Economic Growth .” Economic Modelling 88 ( C ): 1 – 13 .

Iyigun Murat F. , Owen Ann L. . 2004 . “ Income Inequality, Financial Development, and Macroeconomic Fluctuations .” The Economic Journal 114 ( 495 ): 352 – 76 .

Jain Shail . 1975 . The Size Distribution of Income: A Compilation of Data . Washington, DC : World Bank .

Jäntti Markus , Pirtillä Jukka , Rönkkö Risto . 2020 . “ Redistribution, Inequality, and Growth Revisited: Comment on ‘Redistribution, Inequality, and Growth: New Evidence’ .” WIDER Working Paper 2020/117 . Helsinki : UNU-WIDER .

Jenkins Stephen P. 2015 . “ World Income Inequality Databases: An Assessment of WIID and SWIID .” The Journal of Economic Inequality 13 : 629 – 71 .

Jong-sung You , Khagram Sanjeev . 2005 . “ A Comparative Study of Inequality and Corruption .” American Sociological Review 70 ( 1 ): 136 – 57 .

Kabeer Naila 2015 . “ Gender, Poverty and Inequality: A Brief History of Feminist Contributions in the Field of International Development .” Gender & Development 23 ( 2 ): 189 – 205 .

Kaldor Nicholas . 1956 . “ Theories of Distribution .” The Review of Economic Studies 23 ( 2 ): 83 – 100 .

Karl Terry Lynn . 2000 . “ Economic Inequality and Democracy Instability .” Journal of Democracy 11 ( 1 ): 149 – 56 .

Keefer Philip , Knack Stephen . 2002 . “ Polarization, Politics and Property Rights: Links between Inequality and Growth .” Public Choice 111 : 127 – 54 .

Knack Stephen , Keefer Philip . 1997 . “ Does Inequality Harm Growth Only in Democracies? A Replication and Extension .” American Journal of Political Science 41 ( 1 ): 323 – 32 .

Knowles Stephen . 2005 . “ Inequality and Economic Growth: The Empirical Relationship Reconsidered in the Light of Comparable Data .” Journal of Development Studies 41 ( 1 ): 135 – 59 .

Kotschy Rainer , Sunde Uwe . 2017 . “ Democracy, Inequality, and Institutional Quality .” European Economic Review 91 ( C ): 209 – 28 .

Krieckhaus Jonathan , Son Byunghwan , Bellinger Nisha Mukherjee , Wells Jason M. . 2014 . “ Economic Inequality and Democratic Support .” The Journal of Politics 76 ( 1 ): 139 – 51 .

Kuznets Simon . 1955 . “ Economic Growth and Income Inequality .” The American Economic Review 45 ( 1 ): 1 – 28 .

La Ferrara Eliana . 2002 . “ Inequality and Group Participation: Theory and Evidence from Rural Tanzania .” Journal of Public Economics 85 ( 2 ): 235 – 73 .

Lecaillon Jacques , Paukert Felix , Morrison Christian , Germadis Dimitri . 1984 . Income Distribution and Economic Development: An Analytical Survey . Geneva : International Labour Office .

Leigh Andrew , Christopher Jencks , Smeeding Timothy M. . 2011 . “ Health and Economic Inequality .” In The Oxford Handbook of Economic Inequality , edited by Nolan Brian , Salverda Wiemer , Smeeding Timothy M. , 384 – 405 . New York : Oxford University Press .

Lengfelder Christina 2019 . “ Exploring Dynamics of Inequality in Human Development .” 2019 UNDP Human Development Report, Background Paper NO. 3-2019. United Nations Development Programme (UNDP). Accessed June 6, 2020. http://hdr.undp.org/en/content/exploring-dynamics-inequality-human-development .

Li Hongyi , Zou Heng-fu . 1998 . “ Income Inequality Is Not Harmful for Growth: Theory and Evidence .” Review of Development Economics 2 ( 3 ): 318 – 34 .

Lynch John , Smith George Davey , Harper Sam , Hillemeier Marianne , Ross Nancy , Kaplan George A. , Wolfson Michael . 2004 . “ Is Income Inequality a Determinant of Population Health? Part 1. A Systematic Review .” The Milbank Quarterly 82 ( 1 ): 5 – 99 .

Madsen Jakob B. , Islam Md. Rabiuk , Doucouliagos Hristos . 2018 . “ Inequality, Financial Development and Economic Growth in the OECD, 1870–2011 .” European Economic Review 101 ( C ): 605 – 24 .

Maliniak Daniel , Powers Ryan , Walter Barbara F. . 2013 . “ The Gender Citation Gap in International Relations .” International Organization 67 ( 4 ): 889 – 922 .

Marrero Gustavo A. , Rodríguez Juan G. . 2013 . “ Inequality of Opportunity and Growth .” Journal of Development Economics 104 ( C ): 107 – 22 .

Mayer Susan E. 2001 . “ How Did the Increase in Economic Inequality between 1970 and 1990 Affect Children's Educational Attainment? ” American Journal of Sociology 107 ( 1 ): 1 – 32 .

Mellor Jennifer M. , Milyo Jeffrey . 2002 . “ Income Inequality and Health Status in the United States .” The Journal of Human Resources 37 ( 3 ): 510 – 39 .

Morelli Salvatore , Atkinson Anthony B. . 2015 . “ Inequality and Crises Revisited .” Economia Politica 32 : 31 – 51 .

Murphy Kevin M. , Shleifer Andrei , Vishny Robert . 1989 . “ Income Distribution, Market Size and Industrialization .” The Quarterly Journal of Economics 104 ( 3 ): 537 – 64 .

Neves Pedro Cunha , Silva Sandra Maria Tavares . 2014 . “ Inequality and Growth; Uncovering the Main Conclusions from the Empirics .” The Journal of Development Studies 50 ( 1 ): 1 – 21 .

Niño-Zarazúa Miguel , Roope Lawrence , Tarp Finn . 2017 . “ Global Inequality: Relatively Lower, Absolutely Higher .” Review of Income and Wealth 63 ( 4 ): 661 – 84 .

Nolan Brian , Valenzuela Luis . 2019 . “ Inequality and Its Discontents .” Oxford Review of Economic Policy 35 ( 3 ): 396 – 430 .

O'Donnell Owen , van Doorslaer Eddy , van Ourti Tom . 2015 . “ Health and Inequality .” In Handbook of Income Distribution , Volume 2B, edited by Atkinson Anthony B. , Francois Bourguignon , 1419 – 533 . Amsterdam : Elsevier .

Østby Gudrun . 2013 . “ Inequality and Political Violence: A Review of the Literature .” International Area Studies Review 16 ( 2 ): 206 – 31 .

Ostry Jonathan D. , Berg Andrew , Tsangarides Charalambos G. . 2014 . “ Redistribution, Inequality, and Growth .” IMF Staff Discussion Note SDN/14/02 . Washington, DC : International Monetary Fund .

Panizza Ugo . 2002 . “ Income Inequality and Economic Growth: Evidence from American Data .” Journal of Economic Growth 7 : 25 – 41 .

Partridge Mark D. 1997 . “ Is Inequality Harmful for Growth? Comment .” The American Economic Review 87 ( 5 ): 1019 – 32 .

Paukert Felix . 1973 . “ Income Distribution at Different Levels of Development: A Survey of the Evidence .” International Labour Review 108 : 97 – 125 .

Perotti Roberto . 1996 . “ Growth, Income Distribution, and Democracy: What the Data Say .” Journal of Economic Growth 1 : 149 – 87 .

Persson Torsten , Tabellini Guido . 1994 . “ Is Inequality Harmful for Growth? ” The American Economic Review 84 ( 3 ): 600 – 21 .

Pickett Kate E. , Wilkinson Richard G. . 2015 . “ Income Inequality and Health: A Causal Review .” Social Science & Medicine 128 : 316 – 26 .

Ravallion Martin 2001 . “ Growth, Inequality and Poverty: Looking Beyond Averages .” World Development 29 ( 11 ): 1803 – 15 .

Ravallion Martin . 2012 . “ Why Don't We See Poverty Convergence? ” American Economic Review 102 ( 1 ): 504 – 23 .

Roodman David . 2009 . “ A Note on the Theme of Too Many Instruments .” Oxford Bulletin of Economics and Statistics 71 ( 1 ): 135 – 58 .

Rothstein Bo , Uslaner Eric M. . 2005 . “ All for All: Equality, Corruption, and Social Trust .” World Politics 58 ( 1 ): 41 – 72 .

Rowlingson Karen 2011 . “ Does Income Inequality Cause Health and Social Problems? ” Joseph Rowntree Foundation, York. Accessed October 4, 2021. https://www.jrf.org.uk/file/41359/download?token=VV7qsS1J&filetype=full-report .

Sandmo Agnar . 2015 . “ The Principal Problem in Political Economy: Income Distribution in the History of Economic Thought .” In Handbook of Income Distribution , Volume 2, edited by Atkinson Anthony B. , Bourguignon Francois , 3 – 65 . Amsterdam : Elsevier .

Savoia Antonio , Easaw Joshy , McKay Andrew . 2010 . “ Inequality, Democracy, and Institutions: A Critical Review of Recent Research .” World Development 38 ( 2 ): 142 – 54 .

Schäfer Armin , Schwander Hanna . 2019 . “ ‘Don't Play If You Can't Win’: Does Economic Inequality Undermine Political Equality .” European Political Science Review 11 ( 3 ): 395 – 413 .

Scheve Kenneth , Stasavage David . 2017 . “ Wealth Inequality and Democracy .” Annual Review of Political Science 20 : 451 – 68 .

Scholl Nathalie , Klasen Stephan . 2019 . “ Re-Estimating the Relationship between Inequality and Growth .” Oxford Economic Papers 71 ( 4 ): 824 – 47 .

Solt Frederick . 2008 . “ Economic Inequality and Democratic Political Engagement .” American Journal of Political Science 52 ( 1 ): 48 – 60 .

Solt Frederick . 2009 . “ Standardizing the World Income Inequality Database .” Social Science Quarterly 90 ( 2 ): 231 – 42 .

Solt Frederick . 2020 . “ Measuring Income Inequality across Countries and over Time: The Standardized World Income Inequality Database .” Social Science Quarterly 101 ( 3 ): 1183 – 99 .

Stewart Frances . 2005 . “ Horizontal Inequalities: A Neglected Dimension of Development .” In Wider Perspectives on Global Development , edited by Shorrocks Anthony F. , 101 – 35 . London : Palgrave Macmillan .

Stewart Frances . 2016 . “ Changing Perspectives on Inequality and Development .” Studies in Comparative International Development 51 : 60 – 80 .

Stockemer Daniel , Scruggs Lyle . 2012 . “ Income Inequality, Development and Electoral Turnout—New Evidence on a Burgeoning Debate .” Electoral Studies 31 ( 4 ): 764 – 73 .

Subramanian S.V. , Kawachi Ichiro . 2004 . “ Income Inequality and Health: What Have We Learned So Far? ” Epidemiologic Reviews 26 ( 1 ): 78 – 91 .

Sylwester Kevin 2000 . “ Income Inequality, Education Expenditures, and Growth .” Journal of Development Economics 63 ( 2 ): 379 – 98 .

Tanaka Ryuichi 2003 . “ Inequality as a Determinant of Child Labor .” Economics Letters 80 ( 1 ): 93 – 97 .

Thorbecke Erik 2016 . “ Inequality and the Trade-Off between Efficiency and Equity .” Journal of Human Development and Capabilities 17 ( 3 ): 460 – 64 .

Thorbecke Erik , Charumilind Chutatong . 2002 . “ Economic Inequality and Its Socioeconomic Impact .” World Development 30 ( 9 ): 1477 – 95 .

Uslaner Eric M. 2017 . “ Political Trust, Corruption, and Inequality .” In Handbook on Political Trust , edited by Zmerli Sonja , van der Meer Tom W.G. , 302 – 15 . Chelton : Edward Elgar Publishing .

Voitchovsky Sarah 2005 . “ Does the Profile of Income Inequality Matter for Economic Growth? Distinguishing between the Effects of Inequality in Different Parts of the Income Distribution .” Journal of Economic Growth 10 : 273 – 96 .

Voitchovsky Sarah . 2011 . “ Inequality and Economic Growth .” In The Oxford Handbook of Economic Inequality , edited by Nolan Brian , Salverda Wiemer , Smeeding Timothy M. , 549 – 74 . New York : Oxford University Press .

Wagstaff Adam , van Doorslaer Eddy . 2000 . “ Income Inequality and Health: What Does the Literature Tell Us? ” Annual Review of Public Health 21 : 543 – 67 .

Walraevens Benoît . 2021 . “ Adam Smith's View of Economic Inequality .” Cambridge Journal of Economics 45 ( 1 ): 209 – 24 .

WID.world . 2017 . “ World Inequality Database .” Accessed January 26, 2021. https://wid.world .

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

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

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

Email alerts

Citing articles via.

• Recommend to your Library

Affiliations

• Online ISSN 1468-2486
• Print ISSN 1521-9488
• Copyright © 2024 International Studies Association
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Architecture and Design
• Asian and Pacific Studies
• Business and Economics
• Classical and Ancient Near Eastern Studies
• Computer Sciences
• Cultural Studies
• Engineering
• General Interest
• Geosciences
• Industrial Chemistry
• Islamic and Middle Eastern Studies
• Jewish Studies
• Library and Information Science, Book Studies
• Life Sciences
• Linguistics and Semiotics
• Literary Studies
• Materials Sciences
• Mathematics
• Social Sciences
• Sports and Recreation
• Theology and Religion
• Publish your article
• The role of authors
• Promoting your article
• Abstracting & indexing
• Publishing Ethics
• Why publish with De Gruyter
• How to publish with De Gruyter
• Our book series
• Our subject areas
• Your digital product at De Gruyter
• Contribute to our reference works
• Product information
• Tools & resources
• Product Information
• Promotional Materials
• Orders and Inquiries
• FAQ for Library Suppliers and Book Sellers
• Repository Policy
• Free access policy
• Open Access agreements
• Database portals
• For Authors
• Customer service
• People + Culture
• Journal Management
• How to join us
• Working at De Gruyter
• Mission & Vision
• De Gruyter Foundation
• De Gruyter Ebound
• Our Responsibility
• Partner publishers

Your purchase has been completed. Your documents are now available to view.

Causes and Consequences of Income Inequality – An Overview

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

1 Introduction

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

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

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

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

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

2 Causes of Income Inequality

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

2.1 Globalisation

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

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

2.2 Technology

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

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

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

2.3 Tax Policy

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

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

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

2.4 Executive Pay

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

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

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

2.5 Union Decline

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

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

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

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

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

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

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

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

3 Consequences of Income Inequality

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

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

3.1 Economic

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

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

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

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

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

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

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

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

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

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

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

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

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

4 Conclusions

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

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

Alstadsæter, A., N. Johannesen, and G. Zucman. 2019. “Tax Evasion and Inequality.” American Economic Review 109 (6): 2073–103. Search in Google Scholar

Andrasfay, T., and N. Goldman. 2021. “Reductions in 2020 US Life Expectancy Due to COVID-19 and the Disproportionate Impact on the Black and Latino Populations.” Proceedings of the National Academy of Sciences 118 (5), https://doi.org/10.1073/pnas.2014746118 . Search in Google Scholar

Asaria, M., J. Costa-Font, and F. A. Cowell. 2021. “How Does Exposure to Covid-19 Influence Health and Income Inequality Aversion.” IZA Discussion Paper. no. 14103. Also available at https://ssrn.com/abstract=3785067 . Search in Google Scholar

Atkinson, A. B. 2015. Inequality: What Can Be Done? London: Harvard University Press. Search in Google Scholar

Baccaro, L., and C. Howell. 2011. “A Common Neoliberal Trajectory: The Transformation of Industrial Relations in Advanced Capitalism.” Politics & Society 39 (4): 521–63, https://doi.org/10.1177/0032329211420082 . Search in Google Scholar

Blanden, J., P. Gregg, and S. Machin. 2005. Intergenerational Mobility in Europe and North America . London: Centre for Economic Performance. Search in Google Scholar

Borjas, G. J., R. B. Freeman, and L. F. Katz. 1992. “On the Labor Market Effects of Immigration and Trade.” In Immigration and the Workforce , edited by G. J. Borjas, and R. B. Freeman, 213–44. Chicago: University of Chicago Press. Search in Google Scholar

Brown, R. 2017. The Inequality Crisis: The Facts and What We Can Do About It . Bristol: Polity Press. Search in Google Scholar

Case, A., and A. Deaton. 2020. Deaths of Despair and the Future of Capitalism . Princeton: Princeton University Press. Search in Google Scholar

Center on Budget and Policy Priorities (CBPP) . 2017. Tax Cuts for the Rich Aren’t an Economic Panacea – and Could Hurt Growth. Also available at https://www.cbpp.org/research/federal-tax/tax-cuts-for-the-rich-arent-an-economic-panacea-and-could-hurt-growth . Search in Google Scholar

Checchi, D., and J. Visser. 2009. “Inequality and the Labor Market: Unions.” In The Oxford Handbook of Economic Inequality , edited by B. Nolan, W. Salverda, and T. M. Smeeding, 230–56. Oxford: Oxford University Press. Search in Google Scholar

Cingano, F. 2014. Trends in Income Inequality and its Impact on Economic Growth . OECD Social, Employment and Migration Working Papers, No. 163. Paris: OECD Publishing. Search in Google Scholar

Corak, M. 2013. “Income Inequality, Equality of Opportunity, and Intergenerational Mobility.” Journal of Economic Perspectives 27 (3): 79–102, https://doi.org/10.1257/jep.27.3.79 . Search in Google Scholar

Daly, M. 2016. Killing the Competition: Economic Inequality and Homicide . Oxford: Routledge. Search in Google Scholar

Daly, M., C. Boyce, and A. Wood. 2015. “A Social Rank Explanation of How Money Influences Health.” Health Psychology 34 (3): 222–30, https://doi.org/10.1037/hea0000098 . Search in Google Scholar

Deaton, A. 2013. The Great Escape: Health, Wealth, and the Origins of Inequality . Princeton: Princeton University Press. Search in Google Scholar

Dickerson, S. S., and M. Kemeny. 2004. “Acute Stressors and Cortisol Responses: A Theoretical Integration and Synthesis of Laboratory Research.” Psychological Bulletin 130 (3): 355–91, https://doi.org/10.1037/0033-2909.130.3.355 . Search in Google Scholar

Dorling, D. 2017. The Equality Effect: Improving Life for Everyone . Oxford: New Internationalist Publications Ltd. Search in Google Scholar

Dorling, D. 2018. Do We Need Economic Inequality? Cambridge: Polity Press. Search in Google Scholar

Elliott, L. 2017. “Rising Inequality Threatens World Economy, Says WEF.” The Guardian. Also available at https://www.theguardian.com/business/2017/jan/11/inequality-world-economy-wef-brexit-donald-trump-world-economic-forum-risk-report . Search in Google Scholar

Gabaix, X., and A. Landier. 2008. “Why Has CEO Pay Increased So Much?” Quarterly Journal of Economics 123 (1): 49–100, https://doi.org/10.1162/qjec.2008.123.1.49 . Search in Google Scholar

Hacker, J. S., and P. Pierson. 2010. Winner-Take-All Politics: How Washington Made the Rich Richer – And Turned Its Back on the Middle Class . New York: Simon & Schuster. Search in Google Scholar

Haddow, R. 2013. “Labour Market Income Transfers and Redistribution.” In Inequality and the Fading of Redistributive Politics , edited by K. Banting, and J. Myles, 381–412. Vancouver: UBC Press. Search in Google Scholar

Hager, S. 2020. “Varieties of Top Incomes?” Socio-Economic Review 18 (4): 1175–98. Search in Google Scholar

Heimberger, P. 2020. “Does Economic Globalisation Affect Income Inequality? A Meta‐analysis.” The World Economy 43 (11): 2960–82, https://doi.org/10.1111/twec.13007 . Search in Google Scholar

Hope, D., and J. Limberg. 2020. The Economic Consequences of Major Tax Cuts for the Rich . London: London School of Economics and Political Science. Also available at http://eprints.lse.ac.uk/107919/ . Search in Google Scholar

Hsieh, C.-C., and M. D. Pugh. 1993. “Poverty, Inequality and Violent Crime: a Meta-Analysis of Recent Aggregate Data Studies.” Criminal Justice Review 18 (2): 182–202, https://doi.org/10.1177/073401689301800203 . Search in Google Scholar

Huber, E., J. Huo, and J. D. Stephens. 2019. “Power, Policy, and Top Income Shares.” Socio-Economic Review 17 (2): 231–53, https://doi.org/10.1093/ser/mwx027 . Search in Google Scholar

International Monetary Fund (IMF). 2017. Fiscal Monitor: Tackling Inequality . Washington: IMF. Search in Google Scholar

Jaumotte, F., and C. O. Buitron. 2015. “Power from the People.” Finance & Development 52 (1): 29–31. Search in Google Scholar

Jensen, C., and K. Van Kersbergen. 2017. The Politics of Inequality . London: Palgrave. Search in Google Scholar

Kawachi, I., N. E. Adler, and W. H. Dow. 2010. “Money, Schooling, and Health: Mechanisms and Causal Evidence.” Annals of the New York Academy of Sciences 1186 (1): 56–68, https://doi.org/10.1111/j.1749-6632.2009.05340.x . Search in Google Scholar

Leigh, A., C. Jencks, and T. Smeeding. 2009. “Health and Economic Inequality.” In The Oxford Book of Economic Equality , edited by W. Salverda, B. Nolan, and T. Smeeding, 384–405. Oxford: Oxford University Press. Search in Google Scholar

Marmot, M. 2015. The Health Gap: The Challenge of an Unequal World . London: Bloomsbury. Search in Google Scholar

Merton, R. 1938. “Social Structure and Anomie.” American Sociological Review 3 (5): 672–82, https://doi.org/10.2307/2084686 . Search in Google Scholar

Organisation for Economic Co-operation and Development (OECD) . 2020. “Income Inequality” (Indicator) . Paris: OECD Publishing. Also available at https://data.oecd.org/inequality/income-inequality.htm . Search in Google Scholar

Ostry, J. D., P. Loungani, and D. Furceri. 2016. “ Neoliberalism: Oversold? ” Finance and Development 532: 38–41. Also available at https://www.imf.org/external/pubs/ft/fandd/2016/06/ostry.htm . Search in Google Scholar

Piketty, T. 2014. Capital in the Twenty-First Century . Cambridge: Harvard University Press. Search in Google Scholar

Piketty, T. 2020. Capital and Ideology . Cambridge: Harvard University Press. Search in Google Scholar

Revenga, A. 1992. “Exporting Jobs? The Impact of Import Competition on Employment and Wages in U.S. Manufacturing.” Quarterly Journal of Economics 107 (1): 255–84, https://doi.org/10.2307/2118329 . Search in Google Scholar

Rosenfeld, J., and B. Western. 2011. “Unions, Norms, and the Rise in U.S. Wage Inequality.” American Sociological Review 78 (4): 513–37. Search in Google Scholar

Rowlingson, K. 2011. Does Income Inequality Cause Health and Social Problems? York: Joseph Rowntree Foundation. Search in Google Scholar

Saez, E., and M. Veall. 2005. “The Evolution of High Incomes in Northern America: Lessons from Canadian Evidence.” American Economic Review 95 (3): 831–49, https://doi.org/10.1257/0002828054201404 . Search in Google Scholar

Scheidel, W. 2017. The Great Leveller: Violence and the History of Inequality from the Stone Age to the Twenty-First Century . Princeton: Princeton University Press. Search in Google Scholar

Schrecker, T., and C. Bambra. 2015. How Politics Makes Us Sick: Neoliberal Epidemics . New York: Palgrave Macmillan. Search in Google Scholar

Shaxson, N. 2015. Ten Reasons to Defend the Corporation Tax . London: Tax Justice Network. Also available at http://www.taxjustice.net/wp-content/uploads/2013/04/Ten_Reasons_Full_Report.pdf . Search in Google Scholar

Solt, F. 2020. “Measuring Income Inequality across Countries and over Time: The Standardized World Income Inequality Database.” Social Science Quarterly 101 (3): 1183–99. Version 9.0, https://doi.org/10.1111/ssqu.12795 . Search in Google Scholar

Stiglitz, J. 2013. The Price of Inequality . London: Penguin Books. Search in Google Scholar

The Equality Trust . 2018. “The Spirit Level Data.” London. Also available at https://www.equalitytrust.org.uk/civicrm/contribute/transact?reset=1&id=5 . Search in Google Scholar

Thomas, A. 2016. Republic of Equals: Predistribution and Property-Owning Democracy . Oxford: Oxford University Press. Search in Google Scholar

Tridico, P. 2018. “The Determinants of Income Inequality in OECD Countries.” Cambridge Journal of Economics 42 (4): 1009–42, https://doi.org/10.1093/cje/bex069 . Search in Google Scholar

Visser, J. 2019. ICTWSS Database . Version 6.1. Amsterdam: Amsterdam Institute for Advanced Labour Studies (AIAS), University of Amsterdam. Search in Google Scholar

Whitworth, A. 2012. “Inequality and Crime across England: A Multilevel Modelling Approach.” Social Policy and Society 11 (1): 27–40, https://doi.org/10.1017/s1474746411000388 . Search in Google Scholar

Wilkinson, R. 1996. Unhealthy Societies: The Afflictions of Inequality . London: Routledge. Search in Google Scholar

Wilkinson, R., and K. Pickett. 2009. The Spirit Level: Why Equality is Better for Everyone . London: Penguin Books. Search in Google Scholar

Wilkinson, R., and K. Pickett. 2019. The Inner Level: How More Equal Societies Reduce Stress, Restore Sanity and Improve Everyone’s Well-Being . London: Penguin Books. Search in Google Scholar

Wiwad, D., B. Mercier, P. K. Piff, A. Shariff, and L. B. Aknin. 2021. “Recognizing the Impact of COVID-19 on the Poor Alters Attitudes towards Poverty and Inequality.” Journal of Experimental Social Psychology , https://doi.org/10.1016/j.jesp.2020.104083 . Search in Google Scholar

World Economic Forum. 2020. The Global Social Mobility Report 2020 . Geneva: World Economic Forum. Also available at https://www3.weforum.org/docs/Global_Social_Mobility_Report.pdf . Search in Google Scholar

Zucman, G. 2015. The Hidden Wealth of Nations: The Scourge of Tax Havens . Chicago: University of Chicago Press. Search in Google Scholar

© 2021 Walter de Gruyter GmbH, Berlin/Boston

• X / Twitter

Supplementary Materials

Please login or register with De Gruyter to order this product.

Journal and Issue

Articles in the same issue.

Advertisement

Gender inequality as a barrier to economic growth: a review of the theoretical literature

• Open access
• Published: 15 January 2021
• Volume 19 , pages 581–614, ( 2021 )

Cite this article

You have full access to this open access article

• Manuel Santos Silva 1 &
• Stephan Klasen 1  

53k Accesses

26 Citations

24 Altmetric

Explore all metrics

In this article, we survey the theoretical literature investigating the role of gender inequality in economic development. The vast majority of theories reviewed argue that gender inequality is a barrier to development, particularly over the long run. Among the many plausible mechanisms through which inequality between men and women affects the aggregate economy, the role of women for fertility decisions and human capital investments is particularly emphasized in the literature. Yet, we believe the body of theories could be expanded in several directions.

Similar content being viewed by others

Gender Inequality and Growth in Europe

The effect of gender inequality on economic development: case of african countries.

The Feminization U

Avoid common mistakes on your manuscript.

1 Introduction

Theories of long-run economic development have increasingly relied on two central forces: population growth and human capital accumulation. Both forces depend on decisions made primarily within households: population growth is partially determined by households’ fertility choices (e.g., Becker & Barro 1988 ), while human capital accumulation is partially dependent on parental investments in child education and health (e.g., Lucas 1988 ).

In an earlier survey of the literature linking family decisions to economic growth, Grimm ( 2003 ) laments that “[m]ost models ignore the two-sex issue. Parents are modeled as a fictive asexual human being” (p. 154). Footnote 1 Since then, however, economists are increasingly recognizing that gender plays a fundamental role in how households reproduce and care for their children. As a result, many models of economic growth are now populated with men and women. The “fictive asexual human being” is a dying species. In this article, we survey this rich new landscape in theoretical macroeconomics, reviewing, in particular, micro-founded theories where gender inequality affects economic development.

For the purpose of this survey, gender inequality is defined as any exogenously imposed difference between male and female economic agents that, by shaping their behavior, has implications for aggregate economic growth. In practice, gender inequality is typically modeled as differences between men and women in endowments, constraints, or preferences.

Many articles review the literature on gender inequality and economic growth. Footnote 2 Typically, both the theoretical and empirical literature are discussed, but, in almost all cases, the vast empirical literature receives most of the attention. In addition, some of the surveys examine both sides of the two-way relationship between gender inequality and economic growth: gender equality as a cause of economic growth and economic growth as a cause of gender equality. As a result, most surveys end up only scratching the surface of each of these distinct strands of literature.

There is, by now, a large and insightful body of micro-founded theories exploring how gender equality affects economic growth. In our view, these theories merit a separate review. Moreover, they have not received sufficient attention in empirical work, which has largely developed independently (see also Cuberes & Teignier 2014 ). By reviewing the theoretical literature, we hope to motivate empirical researchers in finding new ways of putting these theories to test. In doing so, our work complements several existing surveys. Doepke & Tertilt ( 2016 ) review the theoretical literature that incorporates families in macroeconomic models, without focusing exclusively on models that include gender inequality, as we do. Greenwood, Guner and Vandenbroucke ( 2017 ), in turn, review the theoretical literature from the opposite direction; they study how macroeconomic models can explain changes in family outcomes. Doepke, Tertilt and Voena ( 2012 ) survey the political economy of women’s rights, but without focusing explicitly on their impact on economic development.

To be precise, the scope of this survey consists of micro-founded macroeconomic models where gender inequality (in endowments, constraints, preferences) affects economic growth—either by influencing the economy’s growth rate or shaping the transition paths between multiple income equilibria. As a result, this survey does not cover several upstream fields of partial-equilibrium micro models, where gender inequality affects several intermediate growth-related outcomes, such as labor supply, education, health. Additionally, by focusing on micro-founded macro models, we do not review studies in heterodox macroeconomics, including the feminist economics tradition using structuralist, demand-driven models. For recent overviews of this literature, see Kabeer ( 2016 ) and Seguino ( 2013 , 2020 ). Overall, we find very little dialogue between the neoclassical and feminist heterodox literatures. In this review, we will show that actually these two traditions have several points of contact and reach similar conclusions in many areas, albeit following distinct intellectual routes.

Although the incorporation of gender in macroeconomic models of economic growth is a recent development, the main gendered ingredients of those models are not new. They were developed in at least two strands of literature. First, since the 1960s, “new home economics” has applied the analytical toolbox of rational choice theory to decisions being made within the boundaries of the family (see, e.g., Becker 1960 , 1981 ). Footnote 3 A second literature strand, mostly based on empirical work at the micro level in developing countries, described clear patterns of gender-specific behavior within households that differed across regions of the developing world (see, e.g., Boserup 1970 ). Footnote 4 As we shall see, most of the (micro-founded) macroeconomic models reviewed in this article use several analytical mechanisms from "new home economics”; these mechanisms can typically rationalize several of the gender-specific regularities observed in early studies of developing countries. The growth theorist is then left to explore the aggregate implications for economic development.

The first models we present focus on gender discrimination in (or on access to) the labor market as a distortionary tax on talent. If talent is randomly distributed in the population, men and women are imperfect substitutes in aggregate production, and, as a consequence, gender inequality (as long as determined by non-market processes) will misallocate talent and lower incentives for female human capital formation. These theories do not rely on typical household functions such as reproduction and childrearing. Therefore, in these models, individuals are not organized into households. We review this literature in section 2 .

From there, we proceed to theories where the household is the unit of analysis. In sections 3 and 4 , we cover models that take the household as given and avoid marriage markets or other household formation institutions. This is a world where marriage (or cohabitation) is universal, consensual, and monogamous; families are nuclear, and spouses are matched randomly. The first articles in this tradition model the household as a unitary entity with joint preferences and interests, and with an efficient and centralized decision making process. Footnote 5 These theories posit how men and women specialize into different activities and how parents interact with their children. Section 3 reviews these theories. Over time, the literature has incorporated intra-household dynamics. Now, family members are allowed to have different preferences and interests; they bargain, either cooperatively or not, over family decisions. Now, the theorist recognizes power asymmetries between family members and analyzes how spouses bargain over decisions. Footnote 6 These articles are surveyed in section 4 .

The final set of articles we survey take into account how households are formed. These theories show how gender inequality can influence economic growth and long-run development through marriage market institutions and family formation patterns. Among other topics, this literature has studied ages at first marriage, relative supply of potential partners, monogamy and polygyny, arranged and consensual marriages, and divorce risk. Upon marriage, these models assume different bargaining processes between the spouses, or even unitary households, but they all recognize, in one way or another, that marriage, labor supply, consumption, and investment decisions are interdependent. We review these theories in section 5 .

Table 1 offers a schematic overview of the literature. To improve readability, the table only includes studies that we review in detail, with articles listed in order of appearance in the text. The table also abstracts from models’ extensions and sensitivity checks, and focuses exclusively on the causal pathways leading from gender inequality to economic growth.

The vast majority of theories reviewed argue that gender inequality is a barrier to economic development, particularly over the long run. The focus on long-run supply-side models reflects a recent effort by growth theorists to incorporate two stylized facts of economic development in the last two centuries: (i) a strong positive association between gender equality and income per capita (Fig. 1 ), and (ii) a strong association between the timing of the fertility transition and income per capita (Fig. 2 ). Footnote 7 Models that endogenize a fertility transition are able to generate a transition from a Malthusian regime of stagnation to a modern regime of sustained economic growth, thus replicating the development experience of human societies in the very long run (e.g., Galor 2005a , b ; Guinnane 2011 ). In contrast, demand-driven models in the heterodox and feminist traditions have often argued that gender wage discrimination and gendered sectoral and occupational segregation can be conducive to economic growth in semi-industrialized export-oriented economies. Footnote 8 In these settings—that fit well the experience of East and Southeast Asian economies—gender wage discrimination in female-intensive export industries reduces production costs and boosts exports, profits, and investment (Blecker & Seguino 2002 ; Seguino 2010 ).

Income level and gender equality. Income is the natural log of per capita GDP (PPP-adjusted). The Gender Development Index is the ratio of gender-specific Human Development Indexes: female HDI/male HDI. Data are for the year 2000. Sources: UNDP

Income level and timing of the fertility transition. Income is the natural log of per capita GDP (PPP-adjusted) in 2000. Years since fertility transition are the number of years between 2000 and the onset year of the fertility decline. See Reher ( 2004 ) for details. Sources: UNDP and Reher ( 2004 )

In most long-run, supply-side models reviewed here, irrespectively of the underlying source of gender differences (e.g., biology, socialization, discrimination), the opportunity cost of women’s time in foregone labor market earnings is lower than that of men. This gender gap in the value of time affects economic growth through two main mechanisms. First, when the labor market value of women’s time is relatively low, women will be in charge of childrearing and domestic work in the family. A low value of female time means that children are cheap. Fertility will be high, and economic growth will be low, both because population growth has a direct negative impact on long-run economic performance and because human capital accumulates at a slower pace (through the quantity-quality trade-off). Second, if parents expect relatively low returns to female education, due to women specializing in domestic activities, they will invest relatively less in the education of girls. In the words of Harriet Martineau, one of the first to describe this mechanism, “as women have none of the objects in life for which an enlarged education is considered requisite, the education is not given” (Martineau 1837 , p. 107). In the long run, lower human capital investments (on girls) lead to slower economic development.

Overall, gender inequality can be conceptualized as a source of inefficiency, to the extent that it results in the misallocation of productive factors, such as talent or labor, and as a source of negative externalities, when it leads to higher fertility, skewed sex ratios, or lower human capital accumulation.

We conclude, in section 6 , by examining the limitations of the current literature and pointing ways forward. Among them, we suggest deeper investigations of the role of (endogenous) technological change on gender inequality, as well as greater attention to the role and interests of men in affecting gender inequality and its impact on growth.

2 Gender discrimination and misallocation of talent

Perhaps the single most intuitive argument for why gender discrimination leads to aggregate inefficiency and hampers economic growth concerns the allocation of talent. Assume that talent is randomly distributed in the population. Then, an economy that curbs women’s access to education, market employment, or certain occupations draws talent from a smaller pool than an economy without such restrictions. Gender inequality can thus be viewed as a distortionary tax on talent. Indeed, occupational choice models with heterogeneous talent (as in Roy 1951 ) show that exogenous barriers to women’s participation in the labor market or access to certain occupations reduce aggregate productivity and per capita output (Cuberes & Teignier 2016 , 2017 ; Esteve-Volart 2009 ; Hsieh, Hurst, Jones and Klenow 2019 ).

Hsieh et al. ( 2019 ) represent the US economy with a model where individuals sort into occupations based on innate ability. Footnote 9 Gender and race identity, however, are a source of discrimination, with three forces preventing women and black men from choosing the occupations best fitting their comparative advantage. First, these groups face labor market discrimination, which is modeled as a tax on wages and can vary by occupation. Second, there is discrimination in human capital formation, with the costs of occupation-specific human capital being higher for certain groups. This cost penalty is a composite term encompassing discrimination or quality differentials in private or public inputs into children’s human capital. The third force are group-specific social norms that generate utility premia or penalties across occupations. Footnote 10

Assuming that the distribution of innate ability across race and gender is constant over time, Hsieh et al. ( 2019 ) investigate and quantify how declines in labor market discrimination, barriers to human capital formation, and changing social norms affect aggregate output and productivity in the United States, between 1960 and 2010. Over that period, their general equilibrium model suggests that around 40 percent of growth in per capita GDP and 90 percent of growth in labor force participation can be attributed to reductions in the misallocation of talent across occupations. Declining in barriers to human capital formation account for most of these effects, followed by declining labor market discrimination. Changing social norms, on the other hand, explain only a residual share of aggregate changes.

Two main mechanisms drive these results. First, falling discrimination improves efficiency through a better match between individual ability and occupation. Second, because discrimination is higher in high-skill occupations, when discrimination decreases, high-ability women and black men invest more in human capital and supply more labor to the market. Overall, better allocation of talent, rising labor supply, and faster human capital accumulation raise aggregate growth and productivity.

Other occupational choice models assuming gender inequality in access to the labor market or certain occupations reach similar conclusions. In addition to the mechanisms in Hsieh et al. ( 2019 ), barriers to women’s work in managerial or entrepreneurial occupations reduce average talent in these positions, resulting in aggregate losses in innovation, technology adoption, and productivity (Cuberes & Teignier 2016 , 2017 ; Esteve-Volart 2009 ). The argument can be readily applied to talent misallocation across sectors (Lee 2020 ). In Lee’s model, female workers face discrimination in the non-agricultural sector. As a result, talented women end up sorting into ill-suited agricultural activities. This distortion reduces aggregate productivity in agriculture. Footnote 11

To sum up, when talent is randomly distributed in the population, barriers to women’s education, employment, or occupational choice effectively reduce the pool of talent in the economy. According to these models, dismantling these gendered barriers can have an immediate positive effect on economic growth.

3 Unitary households: parents and children

In this section, we review models built upon unitary households. A unitary household maximizes a joint utility function subject to pooled household resources. Intra-household decision making is assumed away; the household is effectively a black-box. In this class of models, gender inequality stems from a variety of sources. It is rooted in differences in physical strength (Galor & Weil 1996 ; Hiller 2014 ; Kimura & Yasui 2010 ) or health (Bloom et al. 2015 ); it is embedded in social norms (Hiller 2014 ; Lagerlöf 2003 ), labor market discrimination (Cavalcanti & Tavares 2016 ), or son preference (Zhang, Zhang and Li 1999 ). In all these models, gender inequality is a barrier to long-run economic development.

Galor & Weil ( 1996 ) model an economy with three factors of production: capital, physical labor (“brawn”), and mental labor (“brain”). Men and women are equally endowed with brains, but men have more brawn. In economies starting with very low levels of capital per worker, women fully specialize in childrearing because their opportunity cost in terms of foregone market earnings is lower than men’s. Over time, the stock of capital per worker builds up due to exogenous technological progress. The degree of complementarity between capital and mental labor is higher than that between capital and physical labor; as the economy accumulates capital per worker, the returns to brain rise relative to the returns to brawn. As a result, the relative wages of women rise, increasing the opportunity cost of childrearing. This negative substitution effect dominates the positive income effect on the demand for children and fertility falls. Footnote 12 As fertility falls, capital per worker accumulates faster creating a positive feedback loop that generates a fertility transition and kick starts a process of sustained economic growth.

The model has multiple stable equilibria. An economy starting from a low level of capital per worker is caught in a Malthusian poverty trap of high fertility, low income per capita, and low relative wages for women. In contrast, an economy starting from a sufficiently high level of capital per worker will converge to a virtuous equilibrium of low fertility, high income per capita, and high relative wages for women. Through exogenous technological progress, the economy can move from the low to the high equilibrium.

Gender inequality in labor market access or returns to brain can slow down or even prevent the escape from the Malthusian equilibrium. Wage discrimination or barriers to employment would work against the rise of relative female wages and, therefore, slow down the takeoff to modern economic growth.

The Galor and Weil model predicts how female labor supply and fertility evolve in the course of development. First, (married) women start participating in market work and only afterwards does fertility start declining. Historically, however, in the US and Western Europe, the decline in fertility occurred before women’s participation rates in the labor market started their dramatic increase. In addition, these regions experienced a mid-twentieth century baby boom which seems at odds with Galor and Weil’s theory.

Both these stylized facts can be addressed by adding home production to the modeling, as do Kimura & Yasui ( 2010 ). In their article, as capital per worker accumulates, the market wage for brains rises and the economy moves through four stages of development. In the first stage, with a sufficiently low market wage, both husband and wife are fully dedicated to home production and childrearing. The household does not supply labor to the market; fertility is high and constant. In the second stage, as the wage rate increases, men enter the labor market (supplying both brawn and brain), whereas women remain fully engaged in home production and childrearing. But as men partially withdraw from home production, women have to replace them. As a result, their time cost of childrearing goes up. At this stage of development, the negative substitution effect of rising wages on fertility dominates the positive income effect. Fertility starts declining, even though women have not yet entered the labor market. The third stage arrives when men stop working in home production. There is complete specialization of labor by gender; men only do market work, and women only do home production and childrearing. As the market wage rises for men, the positive income effect becomes dominant and fertility increases; this mimics the baby-boom period of the mid-twentieth century. In the fourth and final stage, once sufficient capital is accumulated, women enter the market sector as wage-earners. The negative substitution effect of rising female opportunity costs dominates once again, and fertility declines. The economy moves from a “breadwinner model” to a “dual-earnings model”.

Another important form of gender inequality is discrimination against women in the form of lower wages, holding male and female productivity constant. Cavalcanti & Tavares ( 2016 ) estimate the aggregate effects of wage discrimination using a model-based general equilibrium representation of the US economy. In their model, women are assumed to be more productive in childrearing than men, so they pay the full time cost of this activity. In the labor market, even though men and women are equally productive, women receive only a fraction of the male wage rate—this is the wage discrimination assumption. Wage discrimination works as a tax on female labor supply. Because women work less than they would without discrimination, there is a negative level effect on per capita output. In addition, there is a second negative effect of wage discrimination operating through endogenous fertility. Since lower wages reduce women’s opportunity costs of childrearing, fertility is relatively high, and output per capita is relatively low. The authors calibrate the model to US steady state parameters and estimate large negative output costs of the gender wage gap. Reducing wage discrimination against women by 50 percent would raise per capita income by 35 percent, in the long run.

Human capital accumulation plays no role in Galor & Weil ( 1996 ), Kimura & Yasui ( 2010 ), and Cavalcanti & Tavares ( 2016 ). Each person is exogenously endowed with a unit of brains. The fundamental trade-off in the these models is between the income and substitution effects of rising wages on the demand for children. When Lagerlöf ( 2003 ) adds education investments to a gender-based model, an additional trade-off emerges: that between the quantity and the quality of children.

Lagerlöf ( 2003 ) models gender inequality as a social norm: on average, men have higher human capital than women. Confronted with this fact, parents play a coordination game in which it is optimal for them to reproduce the inequality in the next generation. The reason is that parents expect the future husbands of their daughters to be, on average, relatively more educated than the future wives of their sons. Because, in the model, parents care for the total income of their children’s future households, they respond by investing relatively less in daughters’ human capital. Here, gender inequality does not arise from some intrinsic difference between men and women. It is instead the result of a coordination failure: “[i]f everyone else behaves in a discriminatory manner, it is optimal for the atomistic player to do the same” (Lagerlöf 2003 , p. 404).

With lower human capital, women earn lower wages than men and are therefore solely responsible for the time cost of childrearing. But if, exogenously, the social norm becomes more gender egalitarian over time, the gender gap in parental educational investment decreases. As better educated girls grow up and become mothers, their opportunity costs of childrearing are higher. Parents trade-off the quantity of children by their quality; fertility falls and human capital accumulates. However, rising wages have an offsetting positive income effect on fertility because parents pay a (fixed) “goods cost” per child. The goods cost is proportionally more important in poor societies than in richer ones. As a result, in poor economies, growth takes off slowly because the positive income effect offsets a large chunk of the negative substitution effect. As economies grow richer, the positive income effect vanishes (as a share of total income), and fertility declines faster. That is, growth accelerates over time even if gender equality increases only linearly.

The natural next step is to model how the social norm on gender roles evolves endogenously during the course of development. Hiller ( 2014 ) develops such a model by combining two main ingredients: a gender gap in the endowments of brawn (as in Galor & Weil 1996 ) generates a social norm, which each parental couple takes as given (as in Lagerlöf 2003 ). The social norm evolves endogenously, but slowly; it tracks the gender ratio of labor supply in the market, but with a small elasticity. When the male-female ratio in labor supply decreases, stereotypes adjust and the norm becomes less discriminatory against women.

The model generates a U-shaped relationship between economic development and female labor force participation. Footnote 13 In the preindustrial stage, there is no education and all labor activities are unskilled, i.e., produced with brawn. Because men have a comparative advantage in brawn, they supply more labor to the market than women, who specialize in home production. This gender gap in labor supply creates a social norm that favors boys over girls. Over time, exogenous skill-biased technological progress raises the relative returns to brains, inducing parents to invest in their children’s education. At the beginning, however, because of the social norm, only boys become educated. The economy accumulates human capital and grows, generating a positive income effect that, in isolation, would eventually drive up parental investments in girls’ education. Footnote 14 But endogenous social norms move in the opposite direction. When only boys receive education, the gender gap in returns to market work increases, and women withdraw to home production. As female relative labor supply in the market drops, the social norm becomes more discriminatory against women. As a result, parents want to invest relatively less in their daughters’ education.

In the end, initial conditions determine which of the forces dominates, thereby shaping long-term outcomes. If, initially, the social norm is very discriminatory, its effect is stronger than the income effect; the economy becomes trapped in an equilibrium with high gender inequality and low per capita income. If, on the other hand, social norms are relatively egalitarian to begin with, then the income effect dominates, and the economy converges to an equilibrium with gender equality and high income per capita.

In the models reviewed so far, human capital or brain endowments can be understood as combining both education and health. Bloom et al. ( 2015 ) explicitly distinguish these two dimensions. Health affects labor market earnings because sick people are out of work more often (participation effect) and are less productive per hour of work (productivity effect). Female health is assumed to be worse than male health, implying that women’s effective wages are lower than men’s. As a result, women are solely responsible for childrearing. Footnote 15

The model produces two growth regimes: a Malthusian trap with high fertility and no educational investments; and a regime of sustained growth, declining fertility, and rising educational investments. Once wages reach a certain threshold, the economy goes through a fertility transition and education expansion, taking off from the Malthusian regime to the sustained growth regime.

Female health promotes growth in both regimes, and it affects the timing of the takeoff. The healthier women are, the earlier the economy takes off. The reason is that a healthier woman earns a higher effective wage and, consequently, faces higher opportunity costs of raising children. When female health improves, the rising opportunity costs of children reduce the wage threshold at which educational investments become attractive; the fertility transition and mass education periods occur earlier.

In contrast, improved male health slows down economic growth and delays the fertility transition. When men become healthier, there is only a income effect on the demand for children, without the negative substitution effect (because male childrearing time is already zero). The policy conclusion would be to redistribute health from men to women. However, the policy would impose a static utility cost on the household. Because women’s time allocation to market work is constrained by childrearing responsibilities (whereas men work full-time), the marginal effect of health on household income is larger for men than for women. From the household’s point of view, reducing the gender gap in health produces a trade-off between short-term income maximization and long-term economic development.

In an extension of the model, the authors endogeneize health investments, while keeping the assumption that women pay the full time cost of childrearing. Because women participate less in the labor market (due to childrearing duties), it is optimal for households to invest more in male health. A health gender gap emerges from rational household behavior that takes into account how time-constraints differ by gender; assuming taste-based discrimination against girls or gender-specific preferences is not necessary.

In the models reviewed so far, parents invest in their children’s human capital for purely altruistic reasons. This is captured in the models by assuming that parents derive utility directly from the quantity and quality of children. This is the classical representation of children as durable consumption goods (e.g., Becker 1960 ). In reality, of course, parents may also have egoistic motivations for investing in child quantity and quality. A typical example is that, when parents get old and retire, they receive support from their children. The quantity and quality of children will affect the size of old-age transfers and parents internalize this in their fertility and childcare behavior. According to this view, children are best understood as investment goods.

Zhang et al. ( 1999 ) build an endogenous growth model that incorporates the old-age support mechanism in parental decisions. Another innovative element of their model is that parents can choose the gender of their children. The implicit assumption is that sex selection technologies are freely available to all parents.

At birth, there is a gender gap in human capital endowment, favoring boys over girls. Footnote 16 In adulthood, a child’s human capital depends on the initial endowment and on the parents’ human capital. In addition, the probability that a child survives to adulthood is exogenous and can differ by gender.

Parents receive old-age support from children that survive until adulthood. The more human capital children have, the more old-age support they provide to their parents. Beyond this egoistic motive, parents also enjoy the quantity and the quality of children (altruistic motive). Son preference is modeled by boys having a higher relative weight in the altruistic-component of the parental utility function. In other words, in their enjoyment of children as consumer goods, parents enjoy “consuming” a son more than “consuming” a girl. Parents who prefer sons want more boys than girls. A larger preference for sons, a higher relative survival probability of boys, and a higher human capital endowment of boys positively affect the sex ratio at birth, because, in the parents’ perspective, all these forces increase the marginal utility of boys relative to girls.

Zhang et al. ( 1999 ) show that, if human capital transmission from parents to children is efficient enough, the economy grows endogenously. When boys have a higher human capital endowment than girls, and the survival probability of sons is not smaller than the survival probability of daughters, then only sons provide old-age support. Anticipating this, parents invest more in the human capital of their sons than on the human capital of their daughters. As a result, the gender gap in human capital at birth widens endogenously.

When only boys provide old-age support, an exogenous increase in son preference harms long-run economic growth. The reason is that, when son preference increases, parents enjoy each son relatively more and demand less old-age support from him. Other things equal, parents want to “consume” more sons now and less old-age support later. Because parents want more sons, the sex ratio at birth increases; but because each son provides less old-age support, human capital investments per son decrease (such that the gender gap in human capital narrows). At the aggregate level, the pace of human capital accumulation slows down and, in the long run, economic growth is lower. Thus, an exogenous increase in son preference increases the sex ratio at birth, and reduces human capital accumulation and long-run growth (although it narrows the gender gap in education).

In summary, in growth models with unitary households, gender inequality is closely linked to the division of labor between family members. If women earn relatively less in market activities, they specialize in childrearing and home production, while men specialize in market work. And precisely due to this division of labor, the returns to female educational investments are relatively low. These household behaviors translate into higher fertility and lower human capital and thus pose a barrier to long-run development.

4 Intra-household bargaining: husbands and wives

In this section, we review models populated with non-unitary households, where decisions are the result of bargaining between the spouses. There are two broad types of bargaining processes: non-cooperative, where spouses act independently or interact in a non-cooperative game that often leads to inefficient outcomes (e.g., Doepke & Tertilt 2019 , Heath & Tan 2020 ); and cooperative, where the spouses are assumed to achieve an efficient outcome (e.g., De la Croix & Vander Donckt 2010 ; Diebolt & Perrin 2013 ). As in the previous section, all of these non-unitary models take the household as given, thereby abstracting from marriage markets or other household formation institutions, which will be discussed separately in section 5 . When preferences differ by gender, bargaining between the spouses matters for economic growth. If women care more about child quality than men do and human capital accumulation is the main engine of growth, then empowering women leads to faster economic growth (Prettner & Strulik 2017 ). If, however, men and women have similar preferences but are imperfect substitutes in the production of household public goods, then empowering women has an ambiguous effect on economic growth (Doepke & Tertilt 2019 ).

A separate channel concerns the intergenerational transmission of human capital and woman’s role as the main caregiver of children. If the education of the mother matters more than the education of the father in the production of children’s human capital, then empowering women will be conducive to growth (Agénor 2017 ; Diebolt & Perrin 2013 ), with the returns to education playing a crucial role in the political economy of female empowerment (Doepke & Tertilt 2009 ).

However, different dimensions of gender inequality have different growth impacts along the development process (De la Croix & Vander Donckt 2010 ). Policies that improve gender equality across many dimensions can be particularly effective for economic growth by reaping complementarities and positive externalities (Agénor 2017 ).

The idea that women might have stronger preferences for child-related expenditures than men can be easily incorporated in a Beckerian model of fertility. The necessary assumption is that women place a higher weight on child quality (relative to child quantity) than men do. Prettner & Strulik ( 2017 ) build a unified growth theory model with collective households. Men and women have different preferences, but they achieve efficient cooperation based on (reduced-form) bargaining parameters. The authors study the effect of two types of preferences: (i) women are assumed to have a relative preference for child quality, while men have a relative preference for child quantity; and (ii) parents are assumed to have a relative preference for the education of sons over the education of daughters. In addition, it is assumed that the time cost of childcare borne by men cannot be above that borne by women (but it could be the same).

When women have a relative preference for child quality, increasing female empowerment speeds up the economy’s escape from a Malthusian trap of high fertility, low education, and low income per capita. When female empowerment increases (exogenously), a woman’s relative preference for child quality has a higher impact on household’s decisions. As a consequence, fertility falls, human capital accumulates, and the economy starts growing. The model also predicts that the more preferences for child quality differ between husband and wife, the more effective is female empowerment in raising long-run per capita income, because the sooner the economy escapes the Malthusian trap. This effect is not affected by whether parents have a preference for the education of boys relative to that of girls. If, however, men and women have similar preferences with respect to the quantity and quality of their children, then female empowerment does not affect the timing of the transition to the sustained growth regime.

Strulik ( 2019 ) goes one step further and endogeneizes why men seem to prefer having more children than women. The reason is a different preference for sexual activity: other things equal, men enjoy having sex more than women. Footnote 17 When cheap and effective contraception is not available, a higher male desire for sexual activity explains why men also prefer to have more children than women. In a traditional economy, where no contraception is available, fertility is high, while human capital and economic growth are low. When female bargaining power increases, couples reduce their sexual activity, fertility declines, and human capital accumulates faster. Faster human capital accumulation increases household income and, as a consequence, the demand for contraception goes up. As contraception use increases, fertility declines further. Eventually, the economy undergoes a fertility transition and moves to a modern regime with low fertility, widespread use of contraception, high human capital, and high economic growth. In the modern regime, because contraception is widely used, men’s desire for sex is decoupled from fertility. Both sex and children cost time and money. When the two are decoupled, men prefer to have more sex at the expense of the number of children. There is a reversal in the gender gap in desired fertility. When contraceptives are not available, men desire more children than women; once contraceptives are widely used, men desire fewer children than women. If women are more empowered, the transition from the traditional equilibrium to the modern equilibrium occurs faster.

Both Prettner & Strulik ( 2017 ) and Strulik ( 2019 ) rely on gender-specific preferences. In contrast, Doepke & Tertilt ( 2019 ) are able to explain gender-specific expenditure patterns without having to assume that men and women have different preferences. They set up a non-cooperative model of household decision making and ask whether more female control of household resources leads to higher child expenditures and, thus, to economic development. Footnote 18

In their model, household public goods are produced with two inputs: time and goods. Instead of a single home-produced good (as in most models), there is a continuum of household public goods whose production technologies differ. Some public goods are more time-intensive to produce, while others are more goods-intensive. Each specific public good can only be produced by one spouse—i.e., time and good inputs are not separable. Women face wage discrimination in the labor market, so their opportunity cost of time is lower than men’s. As a result, women specialize in the production of the most time-intensive household public goods (e.g., childrearing activities), while men specialize in the production of goods-intensive household public goods (e.g., housing infrastructure). Notice that, because the household is non-cooperative, there is not only a division of labor between husband and wife, but also a division of decision making, since ultimately each spouse decides how much to provide of his or her public goods.

When household resources are redistributed from men to women (i.e., from the high-wage spouse to the low-wage spouse), women provide more public goods, in relative terms. It is ambiguous, however, whether the total provision of public goods increases with the re-distributive transfer. In a classic model of gender-specific preferences, a wife increases child expenditures and her own private consumption at the expense of the husband’s private consumption. In Doepke & Tertilt ( 2019 ), however, the rise in child expenditures (and time-intensive public goods in general) comes at the expense of male consumption and male-provided public goods.

Parents contribute to the welfare of the next generation in two ways: via human capital investments (time-intensive, typically done by the mother) and bequests of physical capital (goods-intensive, typically done by the father). Transferring resources to women increases human capital, but reduces the stock of physical capital. The effect of such transfers on economic growth depends on whether the aggregate production function is relatively intensive in human capital or in physical capital. If aggregate production is relatively human capital intensive, then transfers to women boost economic growth; if it is relatively intensive in physical capital, then transfers to women may reduce economic growth.

There is an interesting paradox here. On the one hand, transfers to women will be growth-enhancing in economies where production is intensive in human capital. These would be more developed, knowledge intensive, service economies. On the other hand, the positive growth effect of transfers to women increases with the size of the gender wage gap, that is, decreases with female empowerment. But the more advanced, human capital intensive economies are also the ones with more female empowerment (i.e., lower gender wage gaps). In other words, in settings where human capital investments are relatively beneficial, the contribution of female empowerment to human capital accumulation is reduced. Overall, Doepke and Tertilt’s ( 2019 ) model predicts that female empowerment has at best a limited positive effect and at worst a negative effect on economic growth.

Heath & Tan ( 2020 ) argue that, in a non-cooperative household model, income transfers to women may increase female labor supply. Footnote 19 This result may appear counter-intuitive at first, because in collective household models unearned income unambiguously reduces labor supply through a negative income effect. In Heath and Tan’s model, husband and wife derive utility from leisure, consuming private goods, and consuming a household public good. The spouses decide separately on labor supply and monetary contributions to the household public good. Men and women are identical in preferences and behavior, but women have limited control over resources, with a share of their income being captured by the husband. Female control over resources (i.e., autonomy) depends positively on the wife’s relative contribution to household income. Thus, an income transfer to the wife, keeping husband unearned income constant, raises the fraction of her own income that she privately controls. This autonomy effect unambiguously increases women’s labor supply, because the wife can now reap an additional share of her wage bill. Whenever the autonomy effect dominates the (negative) income effect, female labor supply increases. The net effect will be heterogeneous over the wage distribution, but the authors show that aggregate female labor supply is always weakly larger after the income transfer.

Diebolt & Perrin ( 2013 ) assume cooperative bargaining between husband and wife, but do not rely on sex-specific preferences or differences in ability. Men and women are only distinguished by different uses of their time endowments, with females in charge of all childrearing activities. In line with this labor division, the authors further assume that only the mother’s human capital is inherited by the child at birth. On top of the inherited maternal endowment, individuals can accumulate human capital during adulthood, through schooling. The higher the initial human capital endowment, the more effective is the accumulation of human capital via schooling.

A woman’s bargaining power in marriage determines her share in total household consumption and is a function of the relative female human capital of the previous generation. An increase in the human capital of mothers relative to that of fathers has two effects. First, it raises the incentives for human capital accumulation of the next generation, because inherited maternal human capital makes schooling more effective. Second, it raises the bargaining power of the next generation of women and, because women’s consumption share increases, boosts the returns on women’s education. The second effect is not internalized in women’s time allocation decisions; it is an intergenerational externality. Thus, an exogenous increase in women’s bargaining power would promote economic growth by speeding up the accumulation of human capital across overlapping generations.

De la Croix & Vander Donckt ( 2010 ) contribute to the literature by clearly distinguishing between different gender gaps: a gap in the probability of survival, a wage gap, a social and institutional gap, and a gender education gap. The first three are exogenously given, while the fourth is determined within the model.

By assumption, men and women have identical preferences and ability, but women pay the full time cost of childrearing. As in a typical collective household model, bargaining power is partially determined by the spouses’ earnings potential (i.e., their levels of human capital and their wage rates). But there is also a component of bargaining power that is exogenous and captures social norms that discriminate against women—this is the social and institutional gender gap.

Husbands and wives bargain over fertility and human capital investments for their children. A standard Beckerian result emerges: parents invest relatively less in the education of girls, because girls will be more time-constrained than boys and, therefore, the female returns to education are lower in relative terms.

There are at least two regimes in the economy: a corner regime and an interior regime. The corner regime consists of maximum fertility, full gender specialization (no women in the labor market), and large gender gaps in education (no education for girls). Reducing the wage gap or the social and institutional gap does not help the economy escaping this regime. Women are not in labor force, so the wage gap is meaningless. The social and institutional gap will determine women’s share in household consumption, but does not affect fertility and growth. At this stage, the only effective instruments for escaping the corner regime are reducing the gender survival gap or reducing child mortality. Reducing the gender survival gap increases women’s lifespan, which increases their time budget and attracts them to the labor market. Reducing child mortality decreases the time costs of kids, therefore drawing women into the labor market. In both cases, fertility decreases.

In the interior regime, fertility is below the maximum, women’s labor supply is above zero, and both boys and girls receive education. In this regime, with endogenous bargaining power, reducing all gender gaps will boost economic growth. Footnote 20 Thus, depending on the growth regime, some gender gaps affect economic growth, while others do not. Accordingly, the policy-maker should tackle different dimensions of gender inequality at different stages of the development process.

Agénor ( 2017 ) presents a computable general equilibrium that includes many of the elements of gender inequality reviewed so far. An important contribution of the model is to explicitly add the government as an agent whose policies interact with family decisions and, therefore, will impact women’s time allocation. Workers produce a market good and a home good and are organized in collective households. Bargaining power depends on the spouses’ relative human capital levels. By assumption, there is gender discrimination in market wages against women. On top, mothers are exclusively responsible for home production and childrearing, which takes the form of time spent improving children’s health and education. But public investments in education and health also improve these outcomes during childhood. Likewise, public investment in public infrastructure contributes positively to home production. In particular, the ratio of public infrastructure capital stock to private capital stock is a substitute for women’s time in home production. The underlying idea is that improving sanitation, transportation, and other infrastructure reduces time spent in home production. Health status in adulthood depends on health status in childhood, which, in turn, relates positively to mother’s health, her time inputs into childrearing, and government spending. Children’s human capital depends on similar factors, except that mother’s human capital replaces her health as an input. Additionally, women are assumed to derive less utility from current consumption and more utility from children’s health relative to men. Wives are also assumed to live longer than their husbands, which further down-weights female’s emphasis on current consumption. The final gendered assumption is that mother’s time use is biased towards boys. This bias alone creates a gender gap in education and health. As adults, women’s relative lower health and human capital are translated into relative lower bargaining power in household decisions.

Agénor ( 2017 ) calibrates this rich setup for Benin, a low income country, and runs a series of policy experiments on different dimensions of gender inequality: a fall in childrearing costs, a fall in gender pay discrimination, a fall in son bias in mother’s time allocation, and an exogenous increase in female bargaining power. Footnote 21 Interestingly, despite all policies improving gender equality in separate dimensions, not all unambiguously stimulate economic growth. For example, falling childrearing costs raise savings and private investments, which are growth-enhancing, but increase fertility (as children become ‘cheaper’) and reduce maternal time investment per child, thus reducing growth. In contrast, a fall in gender pay discrimination always leads to higher growth, through higher household income that, in turn, boosts savings, tax revenues, and public spending. Higher public spending further contributes to improved health and education of the next generation. Lastly, Agénor ( 2017 ) simulates the effect of a combined policy that improves gender equality in all domains simultaneously. Due to complementarities and positive externalities across dimensions, the combined policy generates more economic growth than the sum of the individual policies. Footnote 22

In the models reviewed so far, men are passive observers of women’s empowerment. Doepke & Tertilt ( 2009 ) set up an interesting political economy model of women’s rights, where men make the decisive choice. Their model is motivated by the fact that, historically, the economic rights of women were expanded before their political rights. Because the granting of economic rights empowers women in the household, and this was done before women were allowed to participate in the political process, the relevant question is why did men willingly share their power with their wives?

Doepke & Tertilt ( 2009 ) answer this question by arguing that men face a fundamental trade-off. On the one hand, husbands would vote for their wives to have no rights whatsoever, because husbands prefer as much intra-household bargaining power as possible. But, on the other hand, fathers would vote for their daughters to have economic rights in their future households. In addition, fathers want their children to marry highly educated spouses, and grandfathers want their grandchildren to be highly educated. By assumption, men and women have different preferences, with women having a relative preference for child quality over quantity. Accordingly, men internalize that, when women become empowered, human capital investments increase, making their children and grandchildren better-off.

Skill-biased (exogenous) technological progress that raises the returns to education over time can shift male incentives along this trade-off. When the returns to education are low, men prefer to make all decisions on their own and deny all rights to women. But once the returns to education are sufficiently high, men voluntarily share their power with women by granting them economic rights. As a result, human capital investments increase and the economy grows faster.

In summary, gender inequality in labor market earnings often implies power asymmetries within the household, with men having more bargaining power than women. If preferences differ by gender and female preferences are more conducive to development, then empowering women is beneficial for growth. When preferences are the same and the bargaining process is non-cooperative, the implications are less clear-cut, and more context-specific. If, in addition, women’s empowerment is curtailed by law (e.g., restrictions on women’s economic rights), then it is important to understand the political economy of women’s rights, in which men are crucial actors.

5 Marriage markets and household formation

Two-sex models of economic growth have largely ignored how households are formed. The marriage market is not explicitly modeled: spouses are matched randomly, marriage is universal and monogamous, and families are nuclear. In reality, however, household formation patterns vary substantially across societies, with some of these differences extending far back in history. For example, Hajnal ( 1965 , 1982 ) described a distinct household formation pattern in preindustrial Northwestern Europe (often referred to as the “European Marriage Pattern”) characterized by: (i) late ages at first marriage for women, (ii) most marriages done under individual consent, and (iii) neolocality (i.e., upon marriage, the bride and the groom leave their parental households to form a new household). In contrast, marriage systems in China and India consisted of: (i) very early female ages at first marriage, (ii) arranged marriages, and (iii) patrilocality (i.e., the bride joins the parental household of the groom).

Economic historians argue that the “European Marriage Pattern” empowered women, encouraging their participation in market activities and reducing fertility levels. While some view this as one of the deep-rooted factors explaining Northwestern Europe’s earlier takeoff to sustained economic growth (e.g., Carmichael, de Pleijt, van Zanden and De Moor 2016 ; De Moor & Van Zanden 2010 ; Hartman 2004 ), others have downplayed the long-run significance of this marriage pattern (e.g., Dennison & Ogilvie 2014 ; Ruggles 2009 ). Despite this lively debate, the topic has been largely ignored by growth theorists. The few exceptions are Voigtländer and Voth ( 2013 ), Edlund and Lagerlöf ( 2006 ), and Tertilt ( 2005 , 2006 ).

After exploring different marriage institutions, we zoom in on contemporary monogamous and consensual marriage and review models where gender inequality affects economic growth through marriage markets that facilitate household formation (Du & Wei 2013 ; Grossbard & Pereira 2015 ; Grossbard-Shechtman 1984 ; Guvenen & Rendall 2015 ). In contrast with the previous two sections, where the household is the starting point of the analysis, the literature on marriage markets and household formation recognizes that marriage, labor supply, and investment decisions are interlinked. The analysis of these interlinkages is sometimes done with unitary households (upon marriage) (Du & Wei 2013 ; Guvenen & Rendall 2015 ), or with non-cooperative models of individual decision-making within households (Grossbard & Pereira 2015 ; Grossbard-Shechtman 1984 ).

Voigtländer and Voth ( 2013 ) argue that the emergence of the “European Marriage Pattern” is a direct consequence of the mid-fourteen century Black Death. They set up a two-sector agricultural economy consisting of physically demanding cereal farming, and less physically demanding pastoral production. The economy is populated by many male and female peasants and by a class of idle, rent-maximizing landlords. Female peasants are heterogeneous with respect to physical strength, but, on average, are assumed to have less brawn relative to male peasants and, thus, have a comparative advantage in the pastoral sector. Both sectors use land as a production input, although the pastoral sector is more land-intensive than cereal production. All land is owned by the landlords, who can rent it out for peasant cereal farming, or use it for large-scale livestock farming, for which they hire female workers. Crucially, women can only work and earn wages in the pastoral sector as long as they are unmarried. Footnote 23 Peasant women decide when to marry and, upon marriage, a peasant couple forms a new household, where husband and wife both work on cereal farming, and have children at a given time frequency. Thus, the only contraceptive method available is delaying marriage. Because women derive utility from consumption and children, they face a trade-off between earned income and marriage.

Initially, the economy rests in a Malthusian regime, where land-labor ratios are relatively low, making the land-intensive pastoral sector unattractive and depressing relative female wages. As a result, women marry early and fertility is high. The initial regime ends in 1348–1350, when the Black Death kills between one third and half of Europe’s population, exogenously generating land abundance and, therefore, raising the relative wages of female labor in pastoral production. Women postpone marriage to reap higher wages, and fertility decreases—moving the economy to a regime of late marriages and low fertility.

In addition to late marital ages and reduced fertility, another important feature of the “European Marriage Pattern” was individual consent for marriage. Edlund and Lagerlöf ( 2006 ) study how rules of consent for marriage influence long-run economic development. In their model, marriages can be formed according to two types of consent rules: individual consent or parental consent. Under individual consent, young people are free to marry whomever they wish, while, under parental consent, their parents are in charge of arranging the marriage. Depending on the prevailing rule, the recipient of the bride-price differs. Under individual consent, a woman receives the bride-price from her husband, whereas, under parental consent, her father receives the bride-price from the father of the groom. Footnote 24 In both situations, the father of the groom owns the labor income of his son and, therefore, pays the bride-price, either directly, under parental consent, or indirectly, under individual consent. Under individual consent, the father needs to transfer resources to his son to nudge him into marrying. Thus, individual consent implies a transfer of resources from the old to the young and from men to women, relative to the rule of parental consent. Redistributing resources from the old to the young boosts long-run economic growth. Because the young have a longer timespan to extract income from their children’s labor, they invest relatively more in the human capital of the next generation. In addition, under individual consent, the reallocation of resources from men to women can have additional positive effects on growth, by increasing women’s bargaining power (see section 4 ), although this channel is not explicitly modeled in Edlund and Lagerlöf ( 2006 ).

Tertilt ( 2005 ) explores the effects of polygyny on long-run development through its impact on savings and fertility. In her model, parental consent applies to women, while individual consent applies to men. There is a competitive marriage market where fathers sell their daughters and men buy their wives. As each man is allowed (and wants) to marry several wives, a positive bride-price emerges in equilibrium. Footnote 25 Upon marriage, the reproductive rights of the bride are transferred from her father to her husband, who makes all fertility decisions on his own and, in turn, owns the reproductive rights of his daughters. From a father’s perspective, daughters are investments goods; they can be sold in the marriage market, at any time. This feature generates additional demand for daughters, which increases overall fertility, and reduces the incentives to save, which decreases the stock of physical capital. Under monogamy, in contrast, the equilibrium bride-price is negative (i.e., a dowry). The reason is that maintaining unmarried daughters is costly for their fathers, so they are better-off paying a (small enough) dowry to their future husbands. In this setting, the economic returns to daughters are lower and, consequently, so is the demand for children. Fertility decreases and savings increase. Thus, moving from polygny to monogamy lowers population growth and raises the capital stock in the long run, which translates into higher output per capita in the steady state.

Instead of enforcing monogamy in a traditionally polygynous setting, an alternative policy is to transfer marriage consent from fathers to daughters. Tertilt ( 2006 ) shows that when individual consent is extended to daughters, such that fathers do not receive the bride-price anymore, the consequences are qualitatively similar to a ban on polygyny. If fathers stop receiving the bride-price, they save more physical capital. In the long run, per capita output is higher when consent is transferred to daughters.

Grossbard-Shechtman ( 1984 ) develops the first non-cooperative model where (monogamous) marriage, home production, and labor supply decisions are interdependent. Footnote 26 Spouses are modeled as separate agents deciding over production and consumption. Marriage becomes an implicit contract for ‘work-in-household’ (WiHo), defined as “an activity that benefits another household member [typically a spouse] who could potentially compensate the individual for these efforts” (Grossbard 2015 , p. 21). Footnote 27 In particular, each spouse decides how much labor to supply to market work and WiHo, and how much labor to demand from the other spouse for WiHo. Through this lens, spousal decisions over the intra-marriage distribution of consumption and WiHo are akin to well-known principal-agent problems faced between firms and workers. In the marriage market equilibrium, a spouse benefiting from WiHo (the principal) must compensate the spouse producing it (the agent) via intra-household transfers (of goods or leisure). Footnote 28 Grossbard-Shechtman ( 1984 ) and Grossbard ( 2015 ) show that, under these conditions, the ratio of men to women (i.e., the sex ratio) in the marriage market is inversely related to female labor supply to the market. The reason is that, as the pool of potential wives shrinks, prospective husbands have to increase compensation for female WiHo. From the potential wife’s point of view, as the equilibrium price for her WiHo increases, market work becomes less attractive. Conversely, when sex ratios are lower, female labor supply outside the home increases. Although the model does not explicit derive growth implications, the relative increase in female labor supply is expected to be beneficial for economic growth, as argued by many of the theories reviewed so far.

In an extension of this framework, Grossbard & Pereira ( 2015 ) analyze how sex ratios affect gendered savings over the marital life-cycle. Assuming that women supply a disproportionate amount of labor for WiHo (due, for example, to traditional gender norms), the authors show that men and women will have very distinct saving trajectories. A higher sex ratio increases savings by single men, who anticipate higher compensation transfers for their wives’ WiHo, whereas it decreases savings by single women, who anticipate receiving those transfers upon marriage. But the pattern flips after marriage: precautionary savings raise among married women, because the possibility of marriage dissolution entails a loss of income from WiHo. The opposite effect happens for married men: marriage dissolution would imply less expenditures in the future. The higher the sex ratio, the higher will be the equilibrium compensation paid by husbands for their wives’ WiHo. Therefore, the sex ratio will positively affect savings among single men and married women, but negatively affect savings among single women and married men. The net effect on the aggregate savings rate and on economic growth will depend on the relative size of these demographic groups.

In a related article, Du & Wei ( 2013 ) propose a model where higher sex ratios worsen marriage markets prospects for young men and their families, who react by increasing savings. Women in turn reduce savings. However, because sex ratios shift the composition of the population in favor of men (high saving type) relative to women (low saving type) and men save additionally to compensate for women’s dis-saving, aggregate savings increase unambiguously with sex ratios.

In Guvenen & Rendall ( 2015 ), female education is, in part, demanded as insurance against divorce risk. The reason is that divorce laws often protect spouses’ future labor market earnings (i.e., returns to human capital), but force them to share their physical assets. Because, in the model, women are more likely to gain custody of their children after divorce, they face higher costs from divorce relative to their husbands. Therefore, the higher the risk of divorce, the more women invest in human capital, as insurance against a future vulnerable economic position. Guvenen & Rendall ( 2015 ) shows that, over time, divorce risk has increased (for example, consensual divorce became replaced by unilateral divorce in most US states in the 1970s). In the aggregate, higher divorce risk boosted female education and female labor supply.

In summary, the rules regulating marriage and household formation carry relevant theoretical consequences for economic development. While the few studies on this topic have focused on age at marriage, consent rules and polygyny, and the interaction between sex ratios, marriage, and labor supply, other features of the marriage market remain largely unexplored (Borella, De Nardi and Yang 2018 ). Growth theorists would benefit from further incorporating theories of household formation in gendered macro models. Footnote 29

6 Conclusion

In this article, we surveyed micro-founded theories linking gender inequality to economic development. This literature offers many plausible mechanisms through which inequality between men and women affects the aggregate economy (see Table 1 ). Yet, we believe the body of theories could be expanded in several directions. We discuss them below and highlight lessons for policy.

The first direction for future research concerns control over fertility. In models where fertility is endogenous, households are always able to achieve their preferred number of children (see Strulik 2019 , for an exception). The implicit assumption is that there is a free and infallible method of fertility control available for all households—a view rejected by most demographers. The gap between desired fertility and achieved fertility can be endogeneized at three levels. First, at the societal level, the diffusion of particular contraceptive methods may be influenced by cultural and religious norms. Second, at the household level, fertility control may be object of non-cooperative bargaining between the spouses, in particular, for contraceptive methods that only women perfectly observe (Ashraf, Field and Lee 2014 ; Doepke & Kindermann 2019 ). More generally, the role of asymmetric information within the household is not yet explored (Walther 2017 ). Third, if parents have preferences over the gender composition of their offspring, fertility is better modeled as a sequential and uncertain process, where household size is likely endogenous to the sex of the last born child (Hazan & Zoabi 2015 ).

A second direction worth exploring concerns gender inequality in a historical perspective. In models with multiple equilibria, an economy’s path is often determined by its initial level of gender equality. Therefore, it would be useful to develop theories explaining why initial conditions varied across societies. In particular, there is a large literature on economic and demographic history documenting how systems of marriage and household formation differed substantially across preindustrial societies (e.g., De Moor & Van Zanden 2010 ; Hajnal 1965 , 1982 ; Hartman 2004 ; Ruggles 2009 ). In our view, more theoretical work is needed to explain both the origins and the consequences of these historical systems.

A third avenue for future research concerns the role of technological change. In several models, technological change is the exogenous force that ultimately erodes gender gaps in education or labor supply (e.g., Bloom et al. 2015 ; Doepke & Tertilt 2009 ; Galor & Weil 1996 ). For that to happen, technological progress is assumed to be skill-biased, thus raising the returns to education—or, in other words, favoring brain over brawn. As such, new technologies make male advantage in physical strength ever more irrelevant, while making female time spent on childrearing and housework ever more expensive. Moreover, recent technological progress increased the efficiency of domestic activities, thereby relaxing women’s time constraints (e.g., Cavalcanti & Tavares 2008 ; Greenwood, Seshadri and Yorukoglu 2005 ). These mechanisms are plausible, but other aspects of technological change need not be equally favorable for women. In many countries, for example, the booming science, technology, and engineering sectors tend to be particularly male-intensive. And Tejani & Milberg ( 2016 ) provide evidence for developing countries that as manufacturing industries become more capital intensive, their female employment share decreases.

Even if current technological progress is assumed to weaken gender gaps, historically, technology may have played exactly the opposite role. If technology today is more complementary to brain, in the past it could have been more complementary to brawn. An example is the plow that, relative to alternative technologies for field preparation (e.g., hoe, digging stick), requires upper body strength, on which men have a comparative advantage over women (Alesina, Giuliano and Nunn 2013 ; Boserup 1970 ). Another, even more striking example, is the invention of agriculture itself—the Neolithic Revolution. The transition from a hunter-gatherer lifestyle to sedentary agriculture involved a relative loss of status for women (Dyble et al. 2015 ; Hansen, Jensen and Skovsgaard 2015 ). One explanation is that property rights on land were captured by men, who had an advantage on physical strength and, consequently, on physical violence. Thus, in the long view of human history, technological change appears to have shifted from being male-biased towards being female-biased. Endogeneizing technological progress and its interaction with gender inequality is a promising avenue for future research.

Fourth, open economy issues are still almost entirely absent. An exception is Rees & Riezman ( 2012 ), who model the effect of globalization on economic growth. Whether global capital flows generate jobs primarily in female or male intensive sectors matters for long-run growth. If globalization creates job opportunities for women, their bargaining power increases and households trade off child quantity by child quality. Fertility falls, human capital accumulates, and long-run per capita output is high. If, on the other hand, globalization creates jobs for men, their intra-household power increases; fertility increases, human capital decreases, and steady-state income per capita is low. The literature would benefit from engaging with open economy demand-driven models of the feminist tradition, such as Blecker & Seguino ( 2002 ), Seguino ( 2010 ). Other fruitful avenues for future research on open economy macro concern gender analysis of global value chains (Barrientos 2019 ), gendered patterns of international migration (Cortes 2015 ; Cortes & Tessada 2011 ), and the diffusion of gender norms through globalization (Beine, Docquier and Schiff 2013 ; Klasen 2020 ; Tuccio & Wahba 2018 ).

A final point concerns the role of men in this literature. In most theoretical models, gender inequality is not the result of an active male project that seeks the domination of women. Instead, inequality emerges as a rational best response to some underlying gender gap in endowments or constraints. Then, as the underlying gap becomes less relevant—for example, due to skill-biased technological change—, men passively relinquish their power (see Doepke & Tertilt 2009 , for an exception). There is never a male backlash against the short-term power loss that necessarily comes with female empowerment. In reality, it is more likely that men actively oppose losing power and resources towards women (Folbre 2020 ; Kabeer 2016 ; Klasen 2020 ). This possibility has not yet been explored in formal models, even though it could threaten the typical virtuous cycle between gender equality and growth. If men are forward-looking, and the short-run losses outweigh the dynamic gains from higher growth, they might ensure that women never get empowered to begin with. Power asymmetries tend to be sticky, because “any group that is able to claim a disproportionate share of the gains from cooperation can develop social institutions to fortify their position” (Folbre 2020 , p. 199). For example, Eswaran & Malhotra ( 2011 ) set up a household decision model where men use domestic violence against their wives as a tool to enhance male bargaining power. Thus, future theories should recognize more often that men have a vested interest on the process of female empowerment.

More generally, policymakers should pay attention to the possibility of a male backlash as an unintended consequence of female empowerment policies (Erten & Keskin 2018 ; Eswaran & Malhotra 2011 ). Likewise, whereas most theories reviewed here link lower fertility to higher economic growth, the relationship is non-monotonic. Fertility levels below the replacement rate will eventually generate aggregate social costs in the form of smaller future workforces, rapidly ageing societies, and increased pressure on welfare systems, to name a few.

Many theories presented in this survey make another important practical point: public policies should recognize that gender gaps in separate dimensions complement and reinforce one another and, therefore, have to be dealt with simultaneously. A naïve policy targeting a single gap in isolation is unlikely to have substantial growth effects in the short run. Typically, inequalities in separate dimensions are not independent from each other (Agénor 2017 ; Bandiera & Does 2013 ; Duflo 2012 ; Kabeer 2016 ). For example, if credit-constrained women face weak property rights, are unable to access certain markets, and have mobility and time constraints, then the marginal return to capital may nevertheless be larger for men. Similarly, the return to male education may well be above the female return if demand for female labor is low or concentrated in sectors with low productivity. In sum, “the fact that women face multiple constraints means that relaxing just one may not improve outcomes” (Duflo 2012 , p. 1076).

Promising policy directions that would benefit from further macroeconomic research are the role of public investments in physical infrastructure and care provision (Agénor 2017 ; Braunstein, Bouhia and Seguino 2020 ), gender-based taxation (Guner, Kaygusuz and Ventura 2012 ; Meier & Rainer 2015 ), and linkages between gender equality and pro-environmental agendas (Matsumoto 2014 ).

See Echevarria & Moe ( 2000 ) for a similar complaint that “theories of economic growth and development have consistently neglected to include gender as a variable” (p. 77).

A non-exhaustive list includes Bandiera & Does ( 2013 ), Braunstein ( 2013 ), Cuberes & Teignier ( 2014 ), Duflo ( 2012 ), Kabeer ( 2016 ), Kabeer & Natali ( 2013 ), Klasen ( 2018 ), Seguino ( 2013 , 2020 ), Sinha et al. ( 2007 ), Stotsky ( 2006 ), World Bank ( 2001 , 2011 ).

For an in-depth history of “new home economics” see Grossbard-Shechtman ( 2001 ) and Grossbard ( 2010 , 2011 ).

For recent empirical reviews see Duflo ( 2012 ) and Doss ( 2013 ).

Although the unitary approach has being rejected on theoretical (e.g., Echevarria & Moe 2000 ; Folbre 1986 ; Knowles 2013 ; Sen 1989 ) and empirical grounds (e.g., Doss 2013 ; Duflo 2003 ; Lundberg et al. 1997 ), these early models are foundational to the subsequent literature. As it turns out, some of the key mechanisms survive in non-unitary theories of the household.

For nice conceptual perspectives on conflict and cooperation in households see Sen ( 1989 ), Grossbard ( 2011 ), and Folbre ( 2020 ).

The relationship depicted in Fig. 1 is robust to using other composite measures of gender equality (e.g., UNDP’s Gender Inequality Index or OECD’s Social Institutions and Gender Index (SIGI) (see Branisa, Klasen and Ziegler 2013 )), and other years besides 2000. In Fig. 2 , the linear prediction explains 56 percent of the cross-country variation in per capita income.

See Seguino ( 2013 , 2020 ) for a review of this literature.

The model allows for sorting on ability (“some people are better teachers”) or sorting on occupation-specific preferences (“others derive more utility from working as a teacher”) (Hsieh et al. 2019 , p. 1441). Here, we restrict our presentation to the case where sorting occurs primarily on ability. The authors find little empirical support for sorting on preferences.

Because the home sector is treated as any other occupation, the model can capture, in a reduced-form fashion, social norms on women’s labor force participation. For example, a social norm on traditional gender roles can be represented as a utility premium obtained by all women working on the home sector.

Note, however, that discrimination against women raises productivity in the non-agricultural sector. The reason is that the few women who end up working outside agriculture are positively selected on talent. Lee ( 2020 ) shows that this countervailing effect is modest and dominated by the loss of productivity in agriculture.

This is not the classic Beckerian quantity-quality trade-off because parents cannot invest in the quality of their children. Instead, the mechanism is built by assumption in the household’s utility function. When women’s wages increase relative to male wages, the substitution effect dominates the income effect.

The hypothesis that female labor force participation and economic development have a U-shaped relationship—known as the feminization-U hypothesis—goes back to Boserup ( 1970 ). See also Goldin ( 1995 ). Recently, Gaddis & Klasen ( 2014 ) find only limited empirical support for the feminization-U.

The model does not consider fertility decisions. Parents derive utility from their children’s human capital (social status utility). When household income increases, parents want to “consume” more social status by investing in their children’s education—this is the positive income effect.

Bloom et al. ( 2015 ) build their main model with unitary households, but show that the key conclusions are robust to a collective representation of the household.

This assumption does not necessarily mean that boys are more talented than girls. It can be also interpreted as a reduced-form way of capturing labor market discrimination against women.

Many empirical studies are in line with this assumption, which is rooted in evolutionary psychology. See Strulik ( 2019 ) for references. There are several other evolutionary arguments for men wanting more children (including with different women). See, among others, Mulder & Rauch ( 2009 ), Penn & Smith ( 2007 ), von Rueden & Jaeggi ( 2016 ). However, for a different view, see Fine ( 2017 ).

They do not model fertility decisions. So there is no quantity-quality trade-off.

In their empirical application, Heath & Tan ( 2020 ) study the Hindu Succession Act, which, through improved female inheritance rights, increased the lifetime unearned income of Indian women. Other policies consistent with the model are, for example, unconditional cash transfers to women.

De la Croix & Vander Donckt ( 2010 ) show this with numerical simulations, because the interior regime becomes analytically intractable.

We focus on gender-related policies in our presentation, but the article simulates additional public policies.

Agénor and Agénor ( 2014 ) develop a similar model, but with unitary households, and Agénor and Canuto ( 2015 ) have a similar model of collective households for Brazil, where adult women can also invest time in human capital formation. Since public infrastructure substitutes for women’s time in home production, more (or better) infrastructure can free up time for female human capital accumulation and, thus, endogenously increase wives’ bargaining power.

Voigtländer and Voth ( 2013 ) justify this assumption arguing that, in England, employment contracts for farm servants working in animal husbandry were conditional on celibacy. However, see Edwards & Ogilvie ( 2018 ) for a critique of this assumption.

The bride-price under individual consent need not be paid explicitly as a lump-sum transfer. It could, instead, be paid to the bride implicitly in the form of higher lifetime consumption.

In Tertilt ( 2005 ), all men are similar (except in age). Widespread polygyny is possible because older men marry younger women and population growth is high. This setup reflects stylized facts for Sub-Saharan Africa. It differs from models that assume male heterogeneity in endowments, where polygyny emerges because a rich male elite owns several wives, while poor men remain single (e.g., Gould, Moav and Simhon 2008 ; Lagerlöf 2005 , 2010 ).

See Grossbard ( 2015 ) for more details and extensions of this model and Grossbard ( 2018 ) for a non-technical overview of the related literature. For an earlier application, see Grossbard ( 1976 ).

The concept of WiHo is closely related but not equivalent to the ‘black-box’ term home production used by much of the literature. It also relates to feminist perspectives on care and social reproduction labor (c.f. Folbre 1994 ).

In the general setup, the model need not lead to a corner solution where only one spouse specializes in WiHo.

For promising approaches, see, among others, Cubeddu and Ríos-Rull ( 2003 ), Goussé, Jacquemet and Robin ( 2017 ), Greenwood, Guner, Kocharkov and Santos ( 2016 ), Guler, Guvenen and Violante ( 2012 ), Walther ( 2017 ), Wong ( 2016 ).

Agénor, P.-R. (2017). A computable overlapping generations model for gender and growth policy analysis. Macroeconomic Dynamics , 21 (1), 11–54.

Article   Google Scholar  

Agénor, P.-R., & Agénor, M. (2014). Infrastructure, women’s time allocation, and economic development. Journal of Economics , 113 (1), 1–30.

Agénor, P.-R., & Canuto, O. (2015). Gender equality and economic growth in Brazil: A long-run analysis. Journal of Macroeconomics , 43 , 155–172.

Alesina, A., Giuliano, P., & Nunn, N. (2013). On the origins of gender roles: women and the plough. Quarterly Journal of Economics , 128 (2), 469–530.

Ashraf, N., Field, E., & Lee, J. (2014). Household bargaining and excess fertility: an experimental study in Zambia. American Economic Review , 104 (7), 2210–2237.

Bandiera, O., & Does, A. N. (2013). Does gender inequality hinder development and economic growth? evidence and policy implications. World Bank Research Observer , 28 (1), 2–21.

Barrientos, S. (2019). Gender and work in global value chains: Capturing the gains? Cambridge: Cambridge University Press.

Becker, G. S. (1960). An economic analysis of fertility. In Demographic and Economic Change in Developed Countries . Princeton: Princeton University Press, pp. 209–240.

Becker, G. S. (1981). A treatise on the family . Cambridge, Massachusetts: Harvard University Press.

Google Scholar  

Becker, G. S., & Barro, R. J. (1988). A reformulation of the economic theory of fertility. Quarterly Journal of Economics , 103 (1), 1–26.

Beine, M., Docquier, F., & Schiff, M. (2013). International migration, transfer of norms and home country fertility. Canadian Journal of Economics , 46 (4), 1406–1430.

Blecker, R. A., & Seguino, S. (2002). Macroeconomic effects of reducing gender wage inequality in an export-oriented, semi-industrialized economy. Review of Development Economics , 6 (1), 103–119.

Bloom, D. E., Kuhn, M., & Prettner, K. (2015). The Contribution of Female Health to Economic Development . NBER Working Paper 21411, National Bureau of Economic Research, Cambridge, MA.

Borella, M., De Nardi, M., & Yang, F. (2018). The aggregate implications of gender and marriage. The Journal of the Economics of Ageing , 11 , 6–26.

Boserup, E. (1970). Woman’s role in economic development . London: George Allen and Unwin Ltd.

Branisa, B., Klasen, S., & Ziegler, M. (2013). Gender inequality in social institutions and gendered development outcomes. World Development , 45 , 252–268.

Braunstein, E. (2013). Gender, growth and employment. Development , 56 (1), 103–113.

Braunstein, E., Bouhia, R., & Seguino, S. (2020). Social reproduction, gender equality and economic growth. Cambridge Journal of Economics , 44 (1), 129–156.

Carmichael, S. G., de Pleijt, A., van Zanden, J. L., & De Moor, T. (2016). The European marriage pattern and its measurement. Journal of Economic History , 76 (01), 196–204.

Cavalcanti, T., & Tavares, J. (2016). The output cost of gender discrimination: a model-based macroeconomics estimate. Economic Journal , 126 (590), 109–134.

Cavalcanti, T. Vd. V., & Tavares, J. (2008). Assessing the "Engines of Liberation”: Home Appliances and Female Labor Force Participation. The Review of Economics and Statistics , 90 (1), 81–88.

Cortes, P. (2015). The feminization of international migration and its effects on the children left behind: evidence from the Philippines. World Development , 65 , 62–78.

Cortes, P., & Tessada, J. (2011). Low-skilled immigration and the labor supply of highly skilled women. American Economic Journal: Applied Economics , 3 (3), 88–123.

Cubeddu, L., & Ríos-Rull, J.-V. (2003). Families as shocks. Journal of the European Economic Association , 1 (2–3), 671–682.

Cuberes, D., & Teignier, M. (2014). Gender inequality and economic growth: a critical review. Journal of International Development , 26 (2), 260–276.

Cuberes, D., & Teignier, M. (2016). Aggregate effects of gender gaps in the labor market: a quantitative estimate. Journal of Human Capital , 10 (1), 1–32.

Cuberes, D., & Teignier, M. (2017). Macroeconomic costs of gender gaps in a model with entrepreneurship and household production. The B.E. Journal of Macroeconomics , 18 (1), 20170031.

De la Croix, D., & VanderDonckt, M. (2010). Would empowering women initiate the demographic transition in least developed countries? Journal of Human Capital , 4 (2), 85–129.

De Moor, T., & Van Zanden, J. L. (2010). Girl power: The European marriage pattern and labour markets in the north sea region in the late medieval and early modern period. Economic History Review , 63 (1), 1–33.

Dennison, T., & Ogilvie, S. (2014). Does the European marriage pattern explain economic growth? Journal of Economic History , 74 (3), 651–693.

Diebolt, C., & Perrin, F. (2013). From stagnation to sustained growth: the role of female empowerment. American Economic Review , 103 (3), 545–549.

Doepke, M., & Kindermann, F. (2019). Bargaining over babies: Theory, evidence, and policy implications. American Economic Review , 109 (9), 3264–3306.

Doepke, M., & Tertilt, M. (2009). Women’s Liberation: What’s in It for Men? Quarterly Journal of Economics , 124 (4), 1541–1591.

Doepke, M., & Tertilt, M. (2016). Families in macroeconomics. In J. B. Taylor and H. Uhlig (eds.), Handbook of Macroeconomics , vol. 2, Amsterdam: Elsevier, pp. 1789–1891.

Doepke, M., & Tertilt, M. (2019). Does female empowerment promote economic development? Journal of Economic Growth , 24 (4), 309–343.

Doepke, M., Tertilt, M., & Voena, A. (2012). The economics and politics of women’s rights. Annual Review of Economics , 4 (1), 339–372.

Doss, C. (2013). Intrahousehold bargaining and resource allocation in developing countries. The World Bank Research Observer , 28 (1), 52–78.

Du, Q., & Wei, S.-J. (2013). A theory of the competitive saving motive. Journal of International Economics , 91 (2), 275–289.

Duflo, E. (2003). Grandmothers and granddaughters: old-age pensions and intrahousehold allocation in South Africa. The World Bank Economic Review , 17 (1), 1–25.

Duflo, E. (2012). Women empowerment and economic development. Journal of Economic Literature , 50 (4), 1051–1079.

Dyble, M., Salali, G. D., Chaudhary, N., Page, A., Smith, D., Thompson, J., Vinicius, L., Mace, R., & Migliano, A. B. (2015). Sex equality can explain the unique social structure of hunter-gatherer bands. Science , 348 (6236), 796–798.

Echevarria, C., & Moe, K. S. (2000). On the need for gender in dynamic models. Feminist Economics , 6 (2), 77–96.

Edlund, L., & Lagerlöf, N.-P. (2006). Individual versus parental consent in marriage: implications for intra-household resource allocation and growth. American Economic Review , 96 (2), 304–307.

Edwards, J., & Ogilvie, S. (2018). Did the Black Death cause economic development by “inventing” fertility restriction? CESifo Working Papers 7016, Munich.

Erten, B., & Keskin, P. (2018). For better or for worse? Education and the prevalence of domestic violence in Turkey. American Economic Journal: Applied Economics , 10 (1), 64–105.

Esteve-Volart, B. (2009). Gender discrimination and growth: theory and evidence from India . Mimeo: York University.

Eswaran, M., & Malhotra, N. (2011). Domestic violence and women’s autonomy in developing countries: theory and evidence. Canadian Journal of Economics , 44 (4), 1222–1263.

Fine, C. (2017). Testosterone rex: Myths of sex, science, and society . New York, NY: WW Norton & Company.

Folbre, N. (1986). Hearts and spades: paradigms of household economics. World Development , 14 (2), 245–255.

Folbre, N. (1994). Who pays for the kids: gender and the structures of constraint . New York: Routledge.

Book   Google Scholar  

Folbre, N. (2020). Cooperation & conflict in the patriarchal labyrinth. Daedalus , 149 (1), 198–212.

Gaddis, I., & Klasen, S. (2014). Economic development, structural change, and women’s labor force participation. Journal of Population Economics , 27 (3), 639–681.

Galor, O. (2005a). From stagnation to growth: unified growth theory. Handbook of Economic Growth , vol. 1, North-Holland: Elsevier, pp. 171–293.

Galor, O. (2005b). The demographic transition and the emergence of sustained economic growth. Journal of the European Economic Association , 3 (2-3), 494–504.

Galor, O., & Weil, D. N. (1996). The gender gap, fertility, and growth. American Economic Review , 86 (3), 374–387.

Goldin, C. (1995). The U-shaped female labor force function in economic development and economic history. In T. P. Schultz (ed.), Investment in Women’s Human Capital and Economic Development . Chicago, IL: University of Chicago Press, pp. 61–90.

Gould, E. D., Moav, O., & Simhon, A. (2008). The mystery of monogamy. American Economic Review , 98 (1), 333–57.

Goussé, M., Jacquemet, N., & Robin, J.-M. (2017). Household labour supply and the marriage market in the UK, 1991-2008. Labour Economics , 46 , 131–149.

Greenwood, J., Guner, N., Kocharkov, G., & Santos, C. (2016). Technology and the changing family: a unified model of marriage, divorce, educational attainment, and married female labor-force participation. American Economic Journal: Macroeconomics , 8 (1), 1–41.

Greenwood, J., Guner, N., & Vandenbroucke, G. (2017). Family economics writ large. Journal of Economic Literature , 55 (4), 1346–1434.

Greenwood, J., Seshadri, A., & Yorukoglu, M. (2005). Engines of liberation. Review of Economic Studies , 72 (1), 109–133.

Grimm, M. (2003). Family and economic growth: a review. Mathematical Population Studies , 10 (3), 145–173.

Grossbard, A. (1976). An economic analysis of polygyny: The case of Maiduguri. Current Anthropology , 17 (4), 701–707.

Grossbard, S. (2010). How “Chicagoan” are Gary Becker’s Economic Models of Marriage? Journal of the History of Economic Thought , 32 (3), 377–395.

Grossbard, S. (2011). Independent individual decision-makers in household models and the New Home Economics. In J. A. Molina (ed.), Household Economic Behaviors . New York, NY: Springer, pp. 41–56.

Grossbard, S. (2015). The Marriage Motive: A Price Theory of Marriage. How Marriage Markets Affect Employment, Consumption, and Savings . New York, NY: Springer.

Grossbard, S. (2018). Marriage and Marriage Markets. In S. L. Averett, L. M. Argys and S. D. Hoffman (eds.), The Oxford Handbook of Women and the Economy . New York, NY: Oxford University Press, pp. 55–74.

Grossbard, S., & Pereira, A. M. (2015). Savings, Marriage, and Work-in-Household. In S. Grossbard, The Marriage Motive . New York, NY: Springer New York, pp. 191–209.

Grossbard-Shechtman, A. (1984). A theory of allocation of time in markets for labour and marriage. The Economic Journal , 94 (376), 863–882.

Grossbard-Shechtman, S. (2001). The new home economics at Colombia and Chicago. Feminist Economics , 7 (3), 103–130.

Guinnane, T. W. (2011). The historical fertility transition: a guide for economists. Journal of Economic Literature , 49 (3), 589–614.

Guler, B., Guvenen, F., & Violante, G. L. (2012). Joint-search theory: new opportunities and new frictions. Journal of Monetary Economics , 59 (4), 352–369.

Guner, N., Kaygusuz, R., & Ventura, G. (2012). Taxation and household labour supply. The Review of Economic Studies , 79 (3), 1113–1149.

Guvenen, F., & Rendall, M. (2015). Women’s emancipation through education: a macroeconomic analysis. Review of Economic Dynamics , 18 (4), 931–956.

Hajnal, J. (1965). European Marriage Patterns in Perspective. In D. V. Glass and D. E. C. Eversley (eds.), Population in History: Essays in Historical Demography , 6 . London: Edward Arnold Ltd, pp. 101–143.

Hajnal, J. (1982). Two kinds of preindustrial household formation system. Population and Development Review , 8 (3), 449–494.

Hansen, C. W., Jensen, P. S., & Skovsgaard, C. V. (2015). Modern gender roles and agricultural history: the neolithic inheritance. Journal of Economic Growth , 20 (4), 365–404.

Hartman, M. S. (2004). The Household and the Making of History: A Subversive View of the Western Past . Cambridge: Cambridge University Press.

Hazan, M., & Zoabi, H. (2015). Sons or daughters? Sex preferences and the reversal of the gender educational gap. Journal of Demographic Economics , 81 (2), 179–201.

Heath, R., & Tan, X. (2020). Intrahousehold bargaining, female autonomy, and labor supply: theory and evidence from India. Journal of the European Economic Association , 18 (4), 1928–1968.

Hiller, V. (2014). Gender inequality, endogenous cultural norms, and economic development. Scandinavian Journal of Economics , 116 (2), 455–481.

Hsieh, C.-T., Hurst, E., Jones, C. I., & Klenow, P. J. (2019). The allocation of talent and US economic growth. Econometrica , 87 (5), 1439–1474.

Kabeer, N. (2016). Gender equality, economic growth, and women’s agency: the “endless variety” and “monotonous similarity” of patriarchal constraints. Feminist Economics , 22 (1), 295–321.

Kabeer, N., & Natali, L. (2013). Gender Equality and Economic Growth: Is there a Win-Win? IDS Working Papers 417. Brighton: Institute of Development Studies.

Kimura, M., & Yasui, D. (2010). The Galor-Weil gender-gap model revisited: from home to market. Journal of Economic Growth , 15 , 323–351.

Klasen, S. (2018). The impact of gender inequality on economic performance in developing countries. Annual Review of Resource Economics , 10 , 279–298.

Klasen, S. (2020). From ‘MeToo’ to Boko Haram: a survey of levels and trends of gender inequality in the world. World Development , 128 , 104862.

Knowles, J. A. (2013). Why are married men working so much? An aggregate analysis of intra-household bargaining and labour supply. Review of Economic Studies , 80 (3), 1055–1085.

Lagerlöf, N.-P. (2003). Gender equality and long-run growth. Journal of Economic Growth , 8 , 403–426.

Lagerlöf, N.-P. (2005). Sex, equality, and growth. Canadian Journal of Economics , 38 (3), 807–831.

Lagerlöf, N.-P. (2010). Pacifying monogamy. Journal of Economic Growth , 15 (3), 235–262.

Lee, M. (2020). Allocation of Female Talent and Cross-Country Productivity Differences . Mimeo: UC San Diego.

Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics , 22 (1), 3–42.

Lundberg, S. J., Pollak, R. A., & Wales, T. J. (1997). Do husbands and wives pool their resources? Evidence from the United Kingdom child benefit. Journal of Human Resources , 32 (3), 463–480.

Martineau, H. (1837). Society in America , vol. 3. London: Saunders & Otley.

Matsumoto, S. (2014). Spouses’ time allocation to pro-environmental activities: Who is saving the environment at home? Review of Economics of the Household , 12 (1), 159–176.

Meier, V., & Rainer, H. (2015). Pigou meets Ramsey: gender-based taxation with non-cooperative couples. European Economic Review , 77 , 28–46.

Mulder, M. B., & Rauch, K. L. (2009). Sexual conflict in humans: variations and solutions. Evolutionary Anthropology: Issues, News, and Reviews , 18 (5), 201–214.

Penn, D. J., & Smith, K. R. (2007). Differential fitness costs of reproduction between the sexes. Proceedings of the National Academy of Sciences , 104 (2), 553–558.

Prettner, K., & Strulik, H. (2017). Gender equity and the escape from poverty. Oxford Economic Papers , 69 (1), 55–74.

Rees, R., & Riezman, R. (2012). Globalization, gender, and growth. Review of Income and Wealth , 58 (1), 107–117.

Reher, D. S. (2004). The demographic transition revisited as a global process. Population, Space and Place , 10 (1), 19–41.

Roy, A. D. (1951). Some thoughts on the distribution of earnings. Oxford Economic Papers , 3 (2), 135–146.

Ruggles, S. (2009). Reconsidering the Northwest European Family System: Living Arrangements of the Aged in Comparative Historical Perspective. Population and Development Review , 35 (2), 249–273.

Seguino, S. (2010). Gender, distribution, and balance of payments constrained growth in developing countries. Review of Political Economy , 22 (3), 373–404.

Seguino, S. (2013). From micro-level gender relations to the macro economy and back again. In D. M. Figart and T. L. Warnecke (eds.), Handbook of Research on Gender and Economic Life . Cheltenham: Edward Elgar Publishing, pp. 325–344.

Seguino, S. (2020). Engendering macroeconomic theory and policy. Feminist Economics , 26 , 27–61.

Sen, A. (1989). Cooperation, inequality, and the family. Population and Development Review , 15 , 61–76.

Sinha, N., Raju, D., & Morrison, A. (2007). Gender equality, poverty and economic growth . World Bank Policy Research Paper 4349. Washington, DC: The World Bank.

Stotsky, J. G. (2006). Gender and its relevance to macroeconomic policy: a survey . IMF Working Paper 06/233. Washington, DC: International Monetary Fund.

Strulik, H. (2019). Desire and development. Macroeconomic Dynamics , 23 (7), 2717–2747.

Tejani, S., & Milberg, W. (2016). Global defeminization? Industrial upgrading and manufacturing employment in developing countries. Feminist Economics , 22 (2), 24–54.

Tertilt, M. (2005). Polygyny, fertility, and savings. Journal of Political Economy , 113 (6), 1341–1371.

Tertilt, M. (2006). Polygyny, women’s rights, and development. Journal of the European Economic Association , 4 , 523–530.

Tuccio, M., & Wahba, J. (2018). Return migration and the transfer of gender norms: evidence from the Middle East. Journal of Comparative Economics , 46 (4), 1006–1029.

Voigtländer, N., & Voth, H.-J. (2013). How the West “invented” fertility restriction. American Economic Review , 103 (6), 2227–2264.

von Rueden, C. R., & Jaeggi, A. V. (2016). Men’s status and reproductive success in 33 nonindustrial societies: effects of subsistence, marriage system and reproductive strategy. Proceedings of the National Academy of Sciences , 113 (39), 10824–10829.

Walther, S. (2017). Moral hazard in marriage: the use of domestic labor as an incentive device. Review of Economics of the Household , 15 (2), 357–382.

Wong, H.-P. C. (2016). Credible commitments and marriage: when the homemaker gets her share at divorce. Journal of Demographic Economics , 82 (3), 241–279.

World Bank (2001). Engendering Development Through Gender Equality in Rights, Resources, and Voice . New York, NY: Oxford University Press.

World Bank (2011). World Development Report 2012: Gender Equality and Development . Washington, DC: The World Bank.

Zhang, J., Zhang, J., & Li, T. (1999). Gender bias and economic development in an endogenous growth model. Journal of Development Economics , 59 (2), 497–525.

Download references

Acknowledgements

We thank the Editor, Shoshana Grossbard, and three anonymous reviewers for helpful comments. We gratefully acknowledge funding from the Growth and Economic Opportunities for Women (GrOW) initiative, a multi-funder partnership between the UK’s Department for International Development, the Hewlett Foundation and the International Development Research Centre. All views expressed here and remaining errors are our own. Manuel dedicates this article to Stephan Klasen, in loving memory.

Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and affiliations.

Department of Economics, University of Goettingen, Platz der Goettinger Sieben 3, 37073, Goettingen, Germany

Manuel Santos Silva & Stephan Klasen

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Manuel Santos Silva .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Santos Silva, M., Klasen, S. Gender inequality as a barrier to economic growth: a review of the theoretical literature. Rev Econ Household 19 , 581–614 (2021). https://doi.org/10.1007/s11150-020-09535-6

Download citation

Received : 27 May 2019

Accepted : 07 December 2020

Published : 15 January 2021

Issue Date : September 2021

DOI : https://doi.org/10.1007/s11150-020-09535-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Gender equality
• Economic growth
• Human capital
• Comparative development

JEL classification

• Find a journal
• Publish with us
• Track your research

How We Understand Economic Inequality and Why It Matters

Economic Mobility Fellow Efraín García Sánchez and colleagues find in new research that beliefs and attitudes about inequality shape how we feel about ourselves, perceive others, and justify inequality. They found:  

• Perceiving higher levels of economic inequality reduces people’s satisfaction with their lives and status in society
• Believing that economic inequality is unfair leads to more extreme feelings, whether positive or negative, toward politicians and partisans 
• Supporting values that foster individual achievement and self-interest make people more tolerant of inequality 

Learn More 

• Perceived Economic Inequality Is Negatively Associated with Subjective Well-being through Status Anxiety and Social Trust | Social Indicators Research  
• Economic Threats, Political and National Identification Predict Affective Polarization: Longitudinal Evidence From Spain | International Review of Social Psychology
• Individual Values Predict Desiring More Economic Inequality: The Moderator Role of Social Mobility | International Journal of Psychology  

Dodson Shares SPARQ Research in Conversation with Forbes

Celebrating International Women's Day

New Work on Culture Change and Combating Racial Disparities in Policing

Research on Economic Inequality

• Recent Chapters

All books in this series (22 titles)

Recent chapters in this series (17 titles)

• ‘Mingling’ the Gini Index and the Mean Income to Rank Countries by Inequality and Social Welfare
• A Multifaceted Approach to Earnings Mobility Comparisons
• COVID-19 Pandemic and Economic Stimulus Policies: Evidence From 156 Economies
• Explaining Income Inequality Trends: An Integrated Approach
• How Much of Intergenerational Immobility Can be Attributed to Differences in Childhood Circumstances?
• Intergenerational Mobility and Life Satisfaction in Spain
• National Versus Regional: Distributional and Poverty Effects of Minimum Income Schemes in Spain
• On Income Inequality in Urban Areas in China During the Period 2002–2013: Comparing the Case of Urban Locals With That of Rural Migrants
• On Measuring ‘Good’ and ‘Bad’ Income Inequality
• Bayesian Inference for Parametric Growth Incidence Curves
• Introduction
• Maximum Inequality: The Case of Categorical Data
• Modeling Household Poverty Status Using Repeated Cross-sectional Surveys
• Multidimensional Poverty and Inclusive Growth in India: An Analysis Using Growth Elasticities and Semi-elasticities
• Multilateral Wellbeing and Inequality Measurement with Ordered Categorical Data: Health, Consumption and the Aging Process in China
• On the Measurement of Health Poverty in the Case of Ordinal Variables: The Case of 29 European Countries in 2009 and 2018
• On the Measurement of Relative, Absolute and Intermediate Pro-middle Class Growth
• Dr Sanghamitra Bandyopadhyay
• Dr Juan Gabriel Rodríguez

We’re listening — tell us what you think

Something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Literature review on income inequality and economic growth

This paper provides a comprehensive literature review of the relationship between income inequality and economic growth. In the theoretical literature, we identified various models in which income inequality is linked to economic growth. They include (i) The level of economic development, (ii) The level of technological development, (iii) Social-political unrest, (iv) The savings rate, (v) The imperfection of credit markets, (vi) The political economy, (vii) Institutions and (viii) The fertility rate. Based on the transmission mechanisms of these models, we found that the relationship between income inequality and growth can be negative, positive or inconclusive. The first three models demonstrate that the relationship is inconclusive, the fourth shows that it is positive, while the remainder indicate that the relationship should be negative. In the face of theoretical ambiguity, we also noted that the empirical findings on the relationship between income inequality and growth are highly debatable. These findings can be broadly classified into four categories, namely negative, positive, inconclusive and no relationship. Based on these findings, we provide a critical survey on methodology issues employed in the prior studies and propose a better methodology to researchers for future studies.

• • Theoretical and empirical literature is reviewed and synthesis is done to understand the income inequality-growth nexus

Graphical Abstract

Specification table

Across countries, the unequal distribution of income and resources among the population is the defining challenge of our time. In both developed and developing economies, the income inequality gap (as measured by the decile ratios and the Gini coefficient based on the Lorenz curve) between rich and poor is at high levels, and continues to rise [24] . When income inequality becomes extremely high, it fuels social dissatisfaction and raises the threat of social and political unrest [13] . In similar vein, Alesina and Perotti [8] :1 argue that high income inequality, “ by increasing the probability of coups, revolutions, mass violence or, more generally, by increasing policy uncertainty and threatening property rights, has a negative effect on investment and, as a consequence, reduces growth ”.

Given the already high level of income inequality and rising trends in many countries, along with the potentially negative consequences for economies, we found that a significant body of literature examines the causes of income inequality and its consequences for economic development. Among them were theoretical analyses of the inequality–growth nexus, which identified various transmission mechanisms linking income inequality to economic growth. These include (i) The level of economic development, (ii) The level of technological development, (iii) Social-political unrest, (iv) The savings rate, (v) The imperfection of credit markets, (vi) The political economy, (vii) Institutions and (viii) The fertility rate. Based on these models, we found that the relationship between income inequality and growth can be negative, positive or inconclusive. Theories on the level of economic development (see [7 , 31 , 38 , 54] ) and technological development (see [6 , 29 , 33] ) demonstrate that the relationship between inequality and growth changes from positive to negative as the level of development increases. Inconclusive results are also echoed by the social-political unrest model, which argues that the socio-political unrest stemming from high income inequality can either inhibit or benefit growth (see [13 , 14 , 56 , 62] ). In addition, theories on the political economy (see [9 , 11 , 13 , 46 , 48 , 50] ), the imperfection of credit markets (see [5 , 12 , 30 , 51] ; Panniza, [46] ), institutions (see [22 , 34 , 61] ) and the fertility rate (see [26] ) demonstrate that income inequality is negatively related to growth. The only theory which supports the positive relationship between income inequality and growth is the theory on the savings rate (see [3 , 17 , 42 , 53] ).

Given such theoretical ambiguity, it is little wonder that the empirical findings on the relationship between income inequality and growth are strongly debated. Early empirical studies by Alesina and Rodrik [9] , Persson and Tabellini [50] and Perotti [49] reported that inequality exerted a negative impact on growth. That negative relationship has been confirmed by numerous subsequent studies (see, for example, Panniza, [18 , 19 , 46 , 55 , 64] ). Evidence of a negative relationship has, however, been challenged by studies which reported positive results on the inequality–growth nexus (see, for example, [28 , 39 , 57 , 58] ). In addition, several studies have yielded inconclusive findings, with most reporting that the relationship is positive in high-income and negative in low-income countries (see, for example, [13 , 20 , 25 , 27] ). A few studies found no relationship between inequality and growth (see [15 , 44] ).

Given the above background, the aim here is to provide a comprehensive literature review of the relationship between income inequality and economic growth, both in theory and empirically. While Section 2 critically analyses the theoretical framework of the income inequality–growth nexus, Section 3 reviews empirical studies on this relationship. Section 4 provides a critical survey on methodology issues employed in the prior studies and proposes a better methodology that can help reconcile the literature. Section 5 concludes the study.

Income inequality and economic growth: Theoretical framework

A theoretical analysis of the inequality–growth nexus has identified various transmission mechanisms in which income inequality is linked to economic growth. These mechanisms are discussed in detail in this section.

The level of economic development

Early researchers explored the link between income inequality and growth through the lens of the developmental stage of the economy. Kuznets [38] documented that the relationship between the two variables relies on the level of economic development of a country, meaning there is a differential relationship between income inequality and growth, with a positive relationship during the early stage of economic development and a negative relationship during the mature stage. This may be attributed to shifts of labor, from one sector to other, developed sectors. For example, when labor moves from the agricultural sector to other sectors of the economy, the per capita income of those individuals increases, as their skills are in demand in those sectors. Individuals who remain in the agricultural sector keep earning a low income, thus income inequality increases during this stage. As the economy develops, with labour continuing to move from agriculture to other sectors, individuals who remain in the agricultural sector will earn higher incomes due to the low supply of labour in that sector. Income inequality thus declines during this stage. Kuznets [38] describes the relationship as an inverted U-hypothesis, which advocates that inequality tends to increase during early stages of economic development and decrease during later stages. This argument is supported by Ahluwalia [7] , Robinson [54] and Gupta and Singh [31] .

The level of technological development

In addition to sectoral change, Galor and Tsiddon [29] , Helpman [33] and Aghion et al. [6] explored the link by connecting income inequality to the developmental stage of technology. During the early stages of technological development, innovative ideas in the economic sector result in increases in income inequality. This is due to the fact that new technology requires highly skilled labor and training, which raises wages in these sectors compared to those sectors which use old technology. As a result, employees in the new sector earn high per-capita incomes, while those working in the sector with old machines continue earning lower incomes. Therefore, income inequality increases during the early stages of technological improvements. However, as the economy moves to the more mature stage of technological development, income inequality decreases, the reason being that as more labour shifts to the sector using new technology, the incomes of those who remained in the sector with old technology also increase due to the low supply in labor in that sector. Therefore, the wage differential gap between them declines, leading to a decrease in income inequality.

The role of technology was probed further by researchers who focused on the Fourth Industrial Revolution (4IR). By implementing modern technologies, 4IR will lead to the continuing automation of traditional manufacturing and industrial processes. As Krueger [37] documented, improvements in technology widen the income inequality gap in the labor market between skilled and lowly skilled labour, because the income of highly skilled labor increases (as those individuals are in demand), while lowly skilled laborers continue earning low incomes. In similar vein, 4IR is skills-biased, which leads to a widening of the income inequality gap [1] . Based on this argument, technological improvements can be harmful to growth, due to concerns about growing inequality and unemployment.

Social-political unrest

Some studies argue that the rise of socio-political unrest, stemming from high income inequality, may dampen growth (see [13 , 14 , 62] ). In countries with extreme wealth and income inequality, there are high levels of social unrest that cause people to engage in strikes, criminality and other unproductive activities. This often results in wastage of government resources and disruptions that threaten the political stability of the country. It causes uncertainty in government and slows down productivity in the economy, while discouraging investment.

By contrast, high income inequality due to the rise of socio-political unrest can promote growth. To reduce the number of strikes, criminal activity, uncertainty and political unrest, politicians and leaders favor redistribution – from the rich to the poor – in the form of a transfer of payments. In turn, this creates a safety net for the population and government to restore society's trust in government. As a result, levels of uncertainty decline and investment increases, prompting an increase in the growth rate in the long run [13 , 14 , 62] . Similarly, Saint-Paul and Verdier [56] demonstrate that, in the presence of high income inequality, the median voter favors a transfer of payments by means of public expenditure, such as financing education. This, in turn, increases human capital for the poor to access education, thereby promoting growth.

The political economy

Political economy models demonstrate that high income inequality may hinder growth (see [9 , 13 , 48] ). The law and government play crucial roles in the economy, with government in charge of the redistribution of income and resources among the population. These models reveal that when the mean income is greater than that of the median voter, people support the redistribution of income and resources (from the rich to the poor). Redistribution takes place through a transfer of payments and public expenditure, such as the establishment of health facilities and the building of schools, among others. This kind of redistribution reduces growth in the long run, however, by discouraging innovation and investment, and causing low productivity [9 , 13 , 48] . In addition, when there is high income inequality, the population demands equal distribution. That sometimes results in riots and other unproductive activities which retard economic growth. Furthermore, factors such as lobbying and rent-seeking, which often occur during the political process, also discourage growth. This happens when those in the upper decile of income distribution prevent the redistribution of income and resources to the poor, resulting in a wastage of government funds and corruption, both of which hamper economic growth in the long run [11 , 46 , 48 , 50] .

The imperfection of credit markets

The imperfect credit markets model demonstrates that income inequality is negatively associated with growth through credit markets (see [4 , 12 , 30 , 51] ; Panniza, [46] ). In an imperfect credit market, a high degree of income inequality limits the poor from accessing credit. Asymmetric information – where the lender and borrower have limited information about each other – inhibits the ability to make well-informed decisions. This limits the ability to borrow and returns on investment. In addition, imperfect laws make it difficult for creditors to collect defaulted loans, because the law might protect the assets of the borrower from being repossessed as collateral. Such laws constrain the collection of debt, leading to the hard terms and conditions faced by potential creditors. This prohibits access to credit for some individuals, in particular the poor. Given that investment depends on how much income and how many assets an individual has, the poor (who only have income for basic necessities) are unable to afford investment opportunities with high returns (for instance, to invest in human capital or property, among others). For this reason, extremely high income inequality reduces investment opportunities, leading to declining growth in the long run.

Existing studies report that income inequality exerts a positive impact on economic growth through savings rates (see [3 , 13 , 17] ). According to these studies, savings are a function of income. As income earned increases, so the savings rate rises, and vice versa. In the presence of high income inequality, rich people earn high incomes which help them to save more, because their marginal propensity to save is relatively high. This increases the aggregate savings, leading to a rise in capital accumulation, thereby enhancing economic growth in the long run (see [3] , [17] , [42] , [53] , [66] ). Following on this argument, Shin [59] demonstrates that the redistribution of income and resources from rich to poor is harmful to growth. Such action reduces the income, wealth and other resources of the rich, leading to a decline in the marginal propensity to save. As a result, aggregate savings and investments decline.

Institutions

Several studies illustrate that income inequality inhibits growth through institutions (see [22 , 34 , 61] ). Institutions play a vital role in the wellbeing of a country, because they are the key drivers of economic growth and development in the long run [2 , 60 , 65] . The quality of institutions is important for distribution and growth outcomes. High income inequality creates fertile ground for bad institutions, and exacerbates inequality and inefficiency, which leads to low growth rates in the long run. In the case of high income inequality, political decisions tend to be biased towards enriching the already rich minority, at the expense of the poor. This results in poor policies, leading to a high level of inefficiency, wastage of state resources, social dissatisfaction and political instability. It further perpetuates inequality and inhibits growth in the long run [34 , 61] . Based on this argument, bad institutions tend to associate with extreme records of inequality, inefficiency and sluggish growth. By contrast, good institutions tend to associate with low inequality, productivity and economic growth.

The fertility rate

Income inequality has been found to negatively affect growth through differences in fertility (see [26] ). This study documented that a widening income inequality gap raises differences in fertility between the rich and the poor in a population. The low-income group usually have many children, and tend to invest less in their children's education due to a lack of financial resources. By contrast, those in the high-income group usually have fewer children and invest more in their education. Therefore, in the case of extreme income inequality, the high fertility differential has a negative impact on human capital, leading to a decline in economic growth.

Income inequality and economic growth: Empirical evidence

Given such theoretical ambiguity, the empirical findings on the relationship between income inequality and growth are also highly debatable. These findings can broadly be classified into four categories, namely negative, positive, inconclusive and no relationship.

Studies with negative results on the relationship between income inequality and economic growth

The earliest empirical studies examining the inequality–growth nexus were conducted in the 1990s, and employed the ordinary least squares (OLS) and two-stage least squares (2SLS) estimation techniques (see [9 , 49 , 50] ). Alesina and Rodrik [9] examined the relationship between distributive politics and economic growth in 46 countries, for the period 1960–1985. They found that higher income inequality was accompanied by low growth. Similarly, Persson and Tabellini [50] examined the impact of inequality on growth in 56 countries, for the period 1960–1985, and found that inequality exerted a negative impact on growth. Using similar estimating techniques, Perotti [49] analysed the relationship between income distribution, democratic institutions and growth in 67 countries, and found that countries with a low level of inequality tended to have high investments in human capital, which then led to economic growth.

Studies in the 2000s developed different estimation techniques to solve the problem at hand. For example, Panizza [46] employed the standard fixed effect (FE) and generalised method of moments (GMM) to reassess the relationship between income inequality and economic growth in the United States for the period 1940–1980. The results of that study documented that income inequality negatively affected economic growth. Another single-country study was conducted on China, where Wan et al. [64] investigated the short- and long-run relationship between inequality and economic growth during the period 1987–2001. By using three-stage least squares, they found that the relationship was nonlinear and negative for China. Recently, Iyke and Ho [35] studied income inequality and growth in Italy, from 1967–2012, using the autoregressive distributed lag (ARDL) estimation technique. Their study found that income inequality affected growth both in the short and long run. That is, income inequality slowed down growth in the country.

In multiple-country studies, Knowles [36] re-examined the relationship between inequality and growth in 40 countries using comparable data and OLS from 1960–1990. That investigation found a negative relationship between inequality and economic growth for the full sample. When the countries were divided according to the income level, he found a significant negative relationship in the low-income countries but an insignificant relationship in high- and middle-income countries. Malinen [41] investigated a sample comprising 60 countries (developed and developing economies) using the Gini index as a measure of income inequality. Panel cointegration methods were used, employing panel dynamic OLS and panel dynamic seemingly unrelated regression (SUR) to analyze the steady state correlation between income inequality and economic development. During the period under study, the findings revealed a negative steady-state correlation between income distribution and economic development. In addition, in developed countries, income inequality was associated with low economic growth in the long run. Another study focused on developed countries: Cingano [23] , for instance, examined the impact of income inequality and economic growth in OECD (Organisation for Economic Co-operation and Development) countries between 1980 and 2012. Employed GMM, the researcher found that in those countries income inequality negatively affected economic growth. Furthermore, the study confirmed human capital as the transmission channel through which income inequality affects growth. Research by Braun et al. [18] , tested the main prediction of their model with respect to the impact of income inequality on growth at different levels of financial development. By using pooled OLS, dynamic panel and instrumental variables (IV) estimations on 150 countries during the period between 1978 and 2012, they found that greater income inequality is associated with lower economic growth. In addition, they also found that such an effect is significantly attenuated when the level of financial development increases in economies. Another study by Royuela et al. [55] tested the income inequality-growth nexus for over 200 comparable regions in 15 OECD countries during 2003–2013. By using the similar estimation techniques of Bruan et al. [18] , they showed a general negative association between inequality and growth in OECD regions. Recently, Breunig and Majeed [19] re-investigated the impact of inequality and economic growth in 152 countries. The study used GMM for the period 1956 to 2011 and found that inequality had a negative effect on growth. They further found that when both poverty and inequality were considered, the negative impact of inequality on growth was concentrated on countries with high rates of poverty.

Studies with positive results on the relationship between income inequality and economic growth

A study which found a positive relationship is that of Partridge [47] , who investigated whether inequality benefited or hindered growth in the United States between 1960 and 1990. That study, which employed OLS, yielded the following results: first, during the period of the study, a positive relationship was found between inequality and economic growth. That is, American states with high inequality grew faster. Second, the study reported that the wellbeing of the median voter had a positive impact on growth. This implies that the unequal distribution of income and resources among the population encouraged economic activity and, in turn, grew the economy. In another single-country study, Rangel et al. [52] focused on growth and income inequality by investigating the linear correlation and inverted-U shape hypothesis in Brazil, from 1991–2000. They found that, in the long run, income inequality and growth tended to move together. The results also confirmed the existence of the inverted-U hypothesis between income inequality and economic growth.

Bhorat and Van der Westhuizen [16] investigated the relationship between economic growth, poverty and inequality in South Africa, for the period 1995–2005. The study employed a distribution-neutral measure, poverty inequality elasticity estimates, and the marginal proportional rate of substitution. During the period under study, the researchers found a shift in the distribution of income and resources during periods of growth, and hence income inequality tended to increase with increases in economic growth. Later, Shahbaz [58] and Majeed [40] both employed the ARDL technique to study the income inequality–growth nexus in Pakistan, with the first investigation spanning the years 1971–2005, and the second, 1975–2013. Both studies identified a positive correlation between income inequality and economic growth in Pakistan during the period under investigation. Majeed [40] further argued that because the poor population did not participate in the growth process, growth became unsustainable.

Studies on multiple countries also reported positive results. For example, Li and Zou [39] re-examined the relation between inequality and growth from 1947–1994 for a group of developed and developing countries. Using FE and RE methods and expanded data, they found that high income inequality resulted in an increase in economic growth. Later, Forbes [28] also re-assessed the inequality–growth relationship in 45 countries, from 1966–1995. With the use of FE and RE, Chamberlain's ᴫ matrix procedure and Arrelano and Bond's GMM, the findings showed that as income increased in the short to medium term, economic growth tended to increase. A recent study by Scholl and Klasen [57] revisited the inequality-growth relationship, paying special attention to the role of transition (post-Soviet) countries. The study was based on the specification used by Forbes [28] on a sample of 122 countries over the period of 1961–2012. By using FE, GMM and IV estimation techniques, they found a positive association between inequality and growth in the overall sample which was driven by transition countries.

Studies with inconclusive results on the relationship between income inequality and economic growth

A number of studies yielded inconclusive findings on the inequality–growth nexus. In particular, most reported that the relationship was positive in high-income countries and negative in the low-income countries. For example, Deininger and Squire [25] employed cross-country samples from 1960–1992 to analyse the influence of inequality (income and distribution of assets) on economic growth, and also studied the effect it exerts on reducing poverty. Using OLS and panel data, that study found that income inequality had a negative effect on future growth. In addition, Deininger and Squire [25] reported that high income inequality reduced the income of the poor and boosted the income of the rich. Barro [13] used 2SLS to study the inequality–growth relationship in a panel of countries for the period 1965–1995. The results showed that, in rich countries, inequality positively affected economic growth, while in poor countries it negatively affected growth during the period under study. This means that, for rich countries, as inequality increased, the economy (as measured by Gross Domestic Product [GDP] per capita) tended to increase as well, while in poor countries, the economy tended to decline as inequality increased.

Studies using GMM methods reached similar results. For example, Voitchovsky [63] analysed the link between income distribution and economic growth in 21 developing countries, from 1975–2000. The findings showed that income inequality had a positive effect on growth at the upper decile of income distribution, while inequality negatively affected growth at the lower decile. Similarly, Castelló-Climent [21] confirmed that the relationship between income and growth was positive in high-income countries and negative in low- and middle-income countries. That study examined the correlation between income and human capital and economic growth across countries during the period 1992–2000. The results further indicated that both income and human capital inequality constrained economic growth for low- and middle-income countries. However, in high-income countries, income and human capital inequality encouraged economic growth during the period under study. In yet another investigation, Fawaz et al. [27] studied the income inequality–growth nexus, focusing on its link to credit constraints in high- and low-income developing countries from 1960–2010. The study found similar results, namely that in low-income developing countries, income inequality is negatively related to economic growth. For high-income developing countries, income inequality was positively related to economic growth.

Halter et al. [32] reported that this relationship changed over time, having studied the relationship across countries from 1965–2005, using GMM. The findings showed that, in the short run, high inequality encouraged economic growth, but over the long run, high inequality slowed down the economy and impeded growth. Likewise, Ostry et al. [45] investigated the link between redistribution, inequality and growth in various countries, and found that net inequality was positively correlated to economic performance during the early stage of economic development, but turned negative during the mature stage. Research by Brueckner and Lederman [20] studied the relationship between inequality and GDP per capita growth. Using panel data from 1970 to 2010, the findings documented that in low income countries transitional growth was positively affected by higher income inequality while such effect turned negative in high income countries.

Studies with evidence of no relationship between income inequality and economic growth

Some studies reported no relationship between income inequality and economic growth. For example, research by Niyimbanira [44] focused on how economic growth affected income inequality from 1996–2014. That study employed the FE method and the pooled regression model, using data from 18 municipalities across the provinces of South Africa. The findings confirmed that economic growth reduced poverty, but had no effect on income inequality, which implies that there was no relationship between income inequality and economic growth. Benos and Karagiannis [15] examined the relationship between top income inequality and growth under the influence of physical and human capital accumulation in the U.S. By using 2SLS and GMM on the annual panel of U.S. state-level data during 1929 to 2013, they concluded that changes in inequality do not have an impact on growth. Table 1 shows the summary of empirical studies discussed in this section.

Summary of empirical studies on the association between income inequality and economic growth

Note: - denotes negative; + denotes positive; 0 denotes no relationship

Methodology

As we have discussed in the previous section, the empirical findings on income inequality and growth are highly inconclusive. In this section, by providing a critical survey on methodology issues employed in the prior studies, we offer possible explanations on the disparity found in the empirical findings, particularly on multiple-countries studies. The early multiple-countries studies [9 , 49 , 50] in general reached a consensus on the negative impact of inequality on growth. Although they used different measures of inequality and samples, they all employed the Ordinary Least Squares (OLS) and Two-Stage Least Squares (2SLS) estimation techniques on cross-section data to estimate the coefficient on the inequality variable.

By the late 1990s, however, the general consensus on the negative relationship between income inequality and growth was challenged by concerns over data quality and the methodological procedures used (see Neves and |Silva, [43] ). With regard to the data quality, some studies argued that the dataset used in the previous studies, which lacked comparability due to the use of different income definitions (gross income versus expenditures) can lead to different results (see [10 , 36] ). According to Knowles [36] , European countries, the U.S. and most of the Latin American countries use gross income data whereas most of the African and Asian countries use expenditure data. Since expenditure is more equally distributed than gross income, such difference in income distribution may lead to a difference in the final results.

Concerning the methodological procedures, there has been a shift on the usage of panel data instead of cross-sectional data in the later studies. Forbes [28] argues that the use of panel data is desirable as it can specifically estimate how a change in a country's level of inequality within a given country will affect growth in that country. In addition, panel data can remove bias from the correlation between time-variant, observable country characteristics and the explanatory variables by controlling for differences in these characteristics. Due to these considerations, many studies started to use panel data (see [13 , 28 , 39] ; among others). However, the use of panel data in the studies may lead to more diverse results. One of the possible explanations is the diversity of estimators employed in the panel studies. While most of the cross-section studies use OLS, panel studies use a wide variety of estimators such as fixed effects, random effects, GMM, etc. Given that these estimators have different underlying assumptions, they are likely to produce different results among the panel studies [43] . Another possible explanation is that, unlike the cross-section data, panel data controls for time-variant, observable country characteristics. Given that the impact of inequality on growth tends to differ across countries and regions, the inter-continental variation contribute a substantial part of the effect. Therefore, the usage of panel data analysis may lead to different results when different samples are used in the studies. With the wider usage of various panel data estimation techniques in the later studies, it is not surprising that we found more diverse results in the inequality-growth literature.

Based on the above considerations, researchers should be more cautious when identifying a general global pattern regarding the inequality-growth relationship. Instead, we propose that more emphasis should be placed on identifying the inequality-growth relationship on a national or regional level. Such an approach will provide a better understanding of the inequality-growth process on the study area by overcoming data comparability constraints and possible methodological challenges.

This paper presented a comprehensive literature review of the relationship between income inequality and economic growth. In the theoretical literature, various transmission mechanisms were identified in which income inequality is linked to economic growth, namely the level of economic development, the level of technological development, social-political unrest, the political economy, the imperfection of credit markets, the savings rate, institutions, and the fertility rate. Based on these models, we found that the relationship between income inequality and growth can be negative, positive or inconclusive. For example, based on the level of economic and technological development, the relationship between inequality and growth is positive and becomes negative as the level of development progresses. Inconclusive results were reported by the social-political unrest model, showing that the rise in socio-political unrest stemming from high-income inequality could either dampen or promote growth. In addition, theories on the political economy, the imperfection of credit markets, institutions and the fertility rate, reported that income inequality was negatively related to growth. The only theory which supported the positive relationship between income inequality and growth was the theory of savings rates.

On the empirical front, we found that numerous studies joined the debate by testing the relationship between income inequality and economic growth. Some found a positive relationship, while others identified a negative impact. Some studies yielded inconclusive findings. In particular, most found that the relationship was positive in high-income countries and negative in low-income countries. Several studies documented no relationship between income inequality and economic growth. In the methodology section, we provided a critical survey on methodology issues employed in the prior studies. We argued that the varying results obtained by these studies can be attributed to empirical aspects such as the data comparability and methodological procedures used. We, therefore, suggest that future studies should place more emphasis on identifying the inequality-growth relationship on a national or regional level to better understand the inequality-growth process on the study area. In addition, we conjecture that as the study countries and time span differed in the empirical studies, the impacts of the various theoretical channels we identified previously could also play a uniquely important role in affecting the relationship of the inequality–growth nexus in those studies. It would be prudent for future studies to apply the theoretical models to provide an in-depth analysis of the existing empirical findings. Such findings, with reference to the social, political and economic structure, would provide more relevant policy recommendations to the countries under study.

Declaration of Competing Interest

The authors of this paper certify that there is no financial or personal interest that influenced the presentation of the paper.

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

Publications

• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Economic Inequality

The hardships and dreams of asian americans living in poverty.

About one-in-ten Asian Americans live in poverty. Pew Research Center conducted 18 focus groups in 12 languages to explore their stories and experiences.

1 in 10: Redefining the Asian American Dream (Short Film)

A booming u.s. stock market doesn’t benefit all racial and ethnic groups equally, wealth surged in the pandemic, but debt endures for poorer black and hispanic families, sign up for our weekly newsletter.

Fresh data delivery Saturday mornings

• Filtering by:

Black Americans’ Views on Success in the U.S.

While Black adults define personal and financial success in different ways, most see these measures of success as major sources of pressure in their lives.

Most Black adults in the U.S. are optimistic about their financial future

68% of Black adults in the U.S. say they do not have enough income to lead the kind of life they want, but a majority are optimistic that they will one day.

Black Americans view capitalism more negatively than positively but express hope in Black businesses

In an August 2022 survey, 54% of Black adults said they had a very or somewhat negative impression of capitalism, up from 40% in May 2019.

Black and White Americans are far apart in their views of reparations for slavery

Overall, 30% of U.S. adults say descendants of people enslaved in the U.S. should be repaid in some way. 68% say they should not be repaid.

Black Americans Have a Clear Vision for Reducing Racism but Little Hope It Will Happen

Black Americans support significant reforms to or complete overhauls of several U.S. institutions to ensure fair treatment. Yet even as they assess inequality and ideas about progress, many are pessimistic about whether society and institutions will change in ways that would reduce racism.

Black and Hispanic Americans, those with less education are more likely to fall out of the middle class each year

In the United States, the transience of economic status varies significantly across racial and ethnic groups and by level of education.

How the American middle class has changed in the past five decades

The share of adults who live in middle-class households fell from 61% in 1971 to 50% in 2021, according to a new analysis.

COVID-19 Pandemic Pinches Finances of America’s Lower- and Middle-Income Families

Nearly one-in-five middle-income families report receiving unemployment benefits in 2020.

A growing share of Americans say affordable housing is a major problem where they live

49% of Americans say the availability of affordable housing in their local community is a major problem, up 10 points from early 2018.

Trust in America: How do Americans view economic inequality?

Amid rising inequality, many Americans feel that the U.S. economic system is unfair and generally favors powerful special interests.

REFINE YOUR SELECTION

Research teams.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Age & Generations
• Coronavirus (COVID-19)
• Economy & Work
• Family & Relationships
• Gender & LGBTQ
• Immigration & Migration
• International Affairs
• Internet & Technology
• Methodological Research
• News Habits & Media
• Non-U.S. Governments
• Other Topics
• Politics & Policy
• Race & Ethnicity
• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

Terms & Conditions

Privacy Policy

Cookie Settings

Reprints, Permissions & Use Policy