thesis of economic inequality

The Impact of Economic Inequality on Social Disparities: A Quantitative Analysis

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2014 Theses Doctoral

Essays on Economic Inequality

Prados, María José

This dissertation consists of three chapters on different aspects of economic inequality. In the first chapter, I study the aggregate implications of health risk and access to health care. At the individual level, health influences earnings potential, while income affects access to medical care. I investigate how this interaction shapes the joint dynamics of inequality in health and earnings over the life cycle, and I measure the redistributive impact of policies that improve access to health care. For that, I introduce health shocks and health care spending in an incomplete markets model with heterogeneous agents. Earnings risk is partially determined within the model due to the health-income feedback, and negative shocks may drive agents into a low income-low health trap, thus magnifying inequality along the life cycle. I estimate the process for health shocks and I calibrate the key parameters of the model using survey data. The calibrated model successfully reproduces the joint dynamics in health and earnings inequality in the life cycle. Like in the data, it predicts that life cycle inequality in health is driven by a sharp decline in health status for the lowest percentiles of the health distribution. I find that the health-income feedback accounts for 9 percent of total earnings inequality at retirement age as measured by the coefficient of variation of earnings, and that it increases by almost seven times the persistence of shocks to productivity. I also find that health care policies that facilitate access to health care have redistributive effects, mostly through earnings improvements for those at the bottom of the earnings distribution. The second chapter, joint with Stefania Albanesi, studies the connection between recent trends in earnings inequality and the behavior of labor supply of married women in the U.S. The entry of married women into the labor force and the rise in women's relative wages are amongst the most notable economic developments of the twentieth century. These phenomena were particularly pronounced in the 1970s and 1980s, when participation of married women grew from 38\% in 1975 to a peak of 60\% in 1996 and the male to female ratio in hourly wages dropped from 1.60 to 1.34. Since the early 1990s, the growth in these indicators has stalled, especially for college graduates. This development is puzzling in light of the continued rise in women's educational investments relative to men and their entry into professional occupations. In this paper, we link the decline in married women's participation and wages relative to trend since the early 1990s to the growth of the skill premium, which substantially accelerated in those years. Our hypothesis is that the growth in wages for highly educated men generated a negative wealth effect on the labor supply of their female spouses, reducing their labor supply and their wages relative to men. Disaggregated evidence on skill premia by gender, gender wage gaps by education and labor force participation of wives provides descriptive support for this mechanism. Specifically, starting in the early 1990s, the growth in the skill premium was lower for women, while convergence in wages across gender slowed more for college graduates. Finally, participation of married women declined starting in the early 1990s especially for college women, women married to men with a college degree or to men in the top percentiles of the earnings distribution. We develop a model of household labor supply which can qualitatively reproduce a negative effect on wives' participation of a rise in husbands' earnings. We show that a calibrated version of the model can account for more than half the decline relative to trend in married women's participation in 1995-2005, and more than two thirds for college women. The model can also account for one third of the rise in the gender wage gap for college graduates relative to trend in the same period. In the third chapter I study the dynamics of earnings risk and inequality over the life cycle for women, and document the gender differences in earnings stochastic processes faced by workers. Female workers have a weaker average attachment to the labor force than their male counterparts, and career interruptions have an impact on earnings. Therefore, it is to be expected that the average earnings process differ by gender, and in this paper I study if this is the case. The main empirical gender asymmetries I find in the profiles of earnings are: i) inequality is lower amongst women than amongst men, ii) inequality peaks twice over the life cycle for women: once during the fertile years, and the again later at retirement age, iii) the differences in inequality evolution between educational groups are larger for men than for women. I estimate the statistical properties of the earnings process, with and without heterogeneity in age profiles, and find that the specification without profile heterogeneity seems to fit the female workers dynamics better.

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Article Contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Diagram with main outcomes of inequality

Source : Authors’ elaboration.

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

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

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

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

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

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

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

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

How Inequality Affects Growth

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

High inequality is growth enhancing

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

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

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

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

High inequality has a negative effect on growth

Credit market imperfections and fertility.

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

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

Taxation and regulatory policies

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

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

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

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

The structure of demand

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

Sociopolitical instability and rent seeking

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

The effect depends

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

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

How Inequality Affects Education and Health

Inequality can have both positive and negative effects on education.

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

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

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

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

Inequality negatively affects health

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

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

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

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

How Inequality Affects Democratic Governance

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

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

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

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

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

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

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

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

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

Direct link

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

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

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

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

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

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

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

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

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

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

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

Channels of transmission

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

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

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

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

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

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

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

Education and Health

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Data quality and availability

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

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

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

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

Concept and measurement of inequality

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

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

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

Estimation methods

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Gutiérrez and Tanaka (2009) review previous theoretical models.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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• Published: 30 March 2020

Higher economic inequality intensifies the financial hardship of people living in poverty by fraying the community buffer

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The current research investigates whether higher economic inequality disproportionately intensifies the financial hardship of low-income individuals. We propose that higher economic inequality increases financial hardship for low-income individuals by reducing their ability to rely on their community as a buffer against financial difficulties. This may occur, in part, because a frayed community buffer reduces low-income individuals’ propensity to seek informal financial support from others. We provide empirical support across eight studies (sample size N  = 1,029,900) from the United States, Australia and rural Uganda, through correlational and experimental data, as well as an instrumental variable analysis. On average across our studies, a 1 s.d. increase in economic inequality is associated with an increase of financial hardship among low-income individuals of 0.10 s.d. We discuss the implications of these results for policies aimed to help people living in poverty buffer against the adverse effects higher economic inequality imposes on them.

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Data availability.

Data are available on the Open Science Framework at the following link: https://osf.io/a3qy8/ . We note that the data providers did not allow us to share the data for study 3 and Supplementary Study 1 . Access to data for study 3 is restricted by the National Centre for Longitudinal Data (NCLD), and can be obtained by submitting a request to the NCLD. Access to data for Supplementary Study 1 is restricted by Gallup, and can be obtained for purchase from Gallup.

Code availability

The code to reproduce the analyses presented in the current research is available on the Open Science Framework at the following link: https://osf.io/a3qy8/ .

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Acknowledgements

We thank C. Prottas for his help in data collection for study 6, and O. Hauser and G. Neszveda for useful discussions. We are also grateful to the Hungarian Fulbright Committee for their support. The authors received no specific funding for this work.

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These authors contributed equally: Jon M. Jachimowicz, Barnabas Szaszi.

Authors and Affiliations

Harvard Business School, Harvard University, Boston, MA, USA

Jon M. Jachimowicz

Psychology Department, Eotvos Lorand University, Budapest, Hungary

Barnabas Szaszi

Edinburgh Business School, Heriot-Watt University, Edinburgh, UK

Marcel Lukas

School of Economics, University of Queensland, Lucia, St Lucia, Queensland, Australia

David Smerdon

Judge Business School, University of Cambridge, Cambridge, UK

Jaideep Prabhu

Woodrow Wilson School, Princeton University, Princeton, NJ, USA

Elke U. Weber

Andlinger Center for Energy and Environment, Princeton University, Princeton, NJ, USA

Department of Psychology, Princeton University, Princeton, NJ, USA

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J.M.J. and B.S. contributed to this manuscript equally and are listed alphabetically. J.M.J. and B.S. conceived of the idea and designed the studies. J.M.J., B.S., M.L. and D.S. collected the data and performed the analysis. J.M.J. and B.S. wrote the paper, and M.L., D.S., J.P. and E.U.W. provided critical revisions.

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Supplementary Tables 1–21, Supplementary Figs. 1–4, Studies 1 and 2, and additional results.

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Jachimowicz, J.M., Szaszi, B., Lukas, M. et al. Higher economic inequality intensifies the financial hardship of people living in poverty by fraying the community buffer. Nat Hum Behav 4 , 702–712 (2020). https://doi.org/10.1038/s41562-020-0849-2

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Received : 21 January 2019

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Published : 30 March 2020

Issue Date : July 2020

DOI : https://doi.org/10.1038/s41562-020-0849-2

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

1. Introduction

2. some basic facts about inequality.

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

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

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

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

4.2. The Instrumental Value of Reducing Inequality

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

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

5. Conclusions

Conflicts of interest.

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

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

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

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Poverty and Economic Inequality

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Definition and causes of poverty, economic inequality and its effects, government policies and interventions, global perspectives on poverty and economic inequality, solutions to poverty and economic inequality.

• World Bank. (2021). Poverty Overview. https://www.worldbank.org/en/topic/poverty/overview

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

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

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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.

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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.

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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.

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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

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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

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Is Economic Inequality Really a Problem?

Yes, but the answer is less obvious than you might think.

By Samuel Scheffler

Dr. Scheffler is a professor of philosophy.

It is impossible to ignore the stark disparities of income and wealth that prevail in this country, and a great many of us are troubled by this state of affairs.

But is economic inequality really what bothers us? An influential essay published in 1987 by the philosopher Harry Frankfurt suggests that we have misidentified the problem. Professor Frankfurt argued that it does not matter whether some people have less than others. What matters is that some people do not have enough. They lack adequate income, have little or no wealth and do not enjoy decent housing, health care or education. If even the worst-off people had enough resources to lead good and fulfilling lives, then the fact that others had still greater resources would not be troubling.

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Professor Frankfurt’s essay didn’t persuade all his fellow philosophers, many of whom remained egalitarians. But his challenge continued to resonate and, in 2015, even as concerns about economic inequality were growing in many corners of society, he published a short book in which he reaffirmed his position.

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Even when the worst-off people are very poor, moreover, it wouldn’t be an improvement to reduce everyone else to their level. Equality would then prevail, but equal misery is hardly an ideal worth striving for.

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