In the wake of the profound shifts in the distribution of income and wealth both within and across countries in the latter half of the 20th century (e.g. Milanovic 2020, Chancel et al. 2021, Kanbur et al. 2022), the relevance of inequality as an object of study has surged and is now back at the heart of economic analysis. This is equally true of the recent growth literature, which has produced a variety of theoretical channels and an enormous volume (and range) of empirical results on the relationship between inequality and growth, which we discuss at length in Baselgia and Foellmi (2022).
The relationship between inequality and growth can be analysed from three angles. First, growth and inequality can be joint outcomes of market interactions and economic policies (e.g. Lundberg and Squire 2003, Dorn and Levell 2022). In particular, government interventions—especially taxation (e.g. Rebelo 1991, Akcigit et al. 2015) and the provision of public goods such as education (e.g. Goldin and Katz 2010)—are arguably key factors affecting inequality and growth simultaneously. Secondly, growth may alter inequality. The Kuznets curve hypothesis is the most famous articulation of this connection and posits an increase in inequality at early stages followed by a decrease at more mature stages of development. Several arguments like demand complementarities or access to investment (e.g. Aghion and Bolton 1997) have been brought forward to explain the latter trickledown mechanism. And thirdly, inequality can affect growth. This is the causal relationship that this column addresses. But, before thinking about how to identify the effect from inequality to growth, one has to decide how to define the two concepts.
Economic inequality can be measured along several dimensions: (top) incomes, wages, consumption, wealth, land, effort, or opportunity. The empirical literature has predominantly used the concept for which the most extensive and highest quality datasets were available, i.e. primarily income (see Table 1 in Baselgia and Foellmi 2022). A priori, there is not a sole dimension of inequality that should be analysed, but the choice should be guided by the theoretical mechanism one has in mind. Most existing theories suggest that the distribution of wealth is crucial for growth, because wealth levels determine investment possibilities and living standards through permanent income. However, there exists a major gap in the existing empirical literature which has so far measured inequality mainly in terms of income. As new and better data on wealth (inequality) become available—see, for instance, the efforts of the World Inequality Database (also Blanchet and Martinez-Toledano 2022)—this gap is expected to be closed soon, at least for industrialised countries.
The other key question regarding inequality is not what to measure, but how to measure it. That is, which statistical measure is most appropriate—in the inequalitygrowth context—to measure the distribution of economic resources: the Gini coefficient, the Theil index, quantile/top shares, percentile ratios, variance, or others? So far, most of the reduced-form literature has employed a single inequality statistic – the Gini coefficient. However, as Voitchovsky (2005) and Litschig and Lombardi (2019) have shown, the use of a single statistic such as the Gini is likely to be inadequate because it masks heterogeneous effects. Simply put, inequalities that arise at the bottom versus the top of the distribution are unlikely to have the same impact on growth. Therefore, and this is the key point we seek to emphasise here, it is unlikely that any single inequality statistic captures the full nature of the inequality-growth relationship. Thus, empirical work should incorporate—at least as a robustness exercise—statistical measures that capture inequality arising from different parts of the distribution.
In the analysis of growth, the fundamental object of interest is how people’s standard of living and welfare evolve over time. The standard measure used for this purpose is the change in real per capita GDP. Although the limitations of GDP as a measure of economic welfare are well established (e.g. Stiglitz et al. 2009, Jones and Klenow 2016, Piketty 2022), for obvious reasons of comparability, it remains the standard metric employed in virtually all of the empirical studies in the literature. Growth performance measured by GDP can in turn be captured in several ways: average growth rates, variability of growth, the length of growth spells, or the potential of growth to ‘take off’ from stagnation to positive growth rates. Most empirical work typically focuses on per capita growth rates over a somewhat arbitrary period of time, say, five or ten years.
By definition, positive per capita growth improves income (and living standards) on average, but not necessarily for everyone in a society, perhaps not even for the majority. Hence, again, we want to emphasise that averages, this time for (income) growth, are unlikely sufficient to truly uncover the inequality-growth nexus.
This limitation of the existing literature to average growth rates, as Van der Weide and Milanovic (2018) rightly point out, seems somewhat paradoxical. After all, one might suppose that we should be particularly interested in how (income or wealth) inequality affects the income trajectories of individuals located at very different points in the income distribution. Using US state-level data for the period 1960 to 2010, Van der Weide and Milanovic (2018) examine whether and how inequality affects income growth differently across various percentiles of the income distribution. In essence, they find that in the US, initial inequality is negatively associated with subsequent growth rates among poorer income percentiles and positively related among higher percentiles. While this is intriguing and relevant evidence, further research along these lines is certainly warranted. First, an obvious next step in order to generalise these results is to extend this type of analysis to other countries and/or in a cross-country framework. Second, a more thorough exploration of the mechanisms that cause inequality to affect growth unevenly along the income distribution is needed.
Connecting the distribution of inequality and growth inequality
The theoretical arguments and the empirical evidence seem to suggest that both very high and very low levels of overall inequality are bad for growth. However, when considering different parts of the distribution, inequality at the top and inequality at the bottom of the (income) distribution have an opposite effect on average income growth. On the other hand, when we want to understand which parts of the distribution profit from growth, an average measure of income inequality produces an opposite effect on income growth between the lower-income and upper-income percentiles. Thus, and this is the main point we are trying to get across in this column, averages are not sufficient to reveal the complexity of the relationship between inequality and growth, and that this applies to both inequality and growth. Eventually, we think the following complex of questions could be fruitful to further research: Is inequality —whatever dimension we are interested in, i.e. incomes, wages, consumption, wealth, land, or opportunity—at the top of the distribution beneficial for the income growth of the top groups, and how does the relationship look like for the bottom part of the distribution?
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Baselgia, E and R Foellmi (2022), “Inequality and growth: a review on a great open debate in economics”, CEPR Discussion Paper No. 17483.
Blanchet, T and C Martínez-Toledano (2022), “Wealth Inequality Dynamics in Europe and the United States: Understanding the Determinants”, Journal of Monetary Economics, forthcoming.
Chancel, L, T Piketty, E Saez, G Zucman et al. (2021), World Inequality Report 2022, World Inequality Lab.
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