Despite extensive research controversy remains on the effects of income inequality on economic growth. The literature proposes several transmission channels through which these effects may occur and even the existence of two different forms of inequality. However, empirical studies have not generally distinguished between these channels, nor have they considered the two forms of inequality and their separate effects on growth. In this paper, we review the theory and the evidence of the transmission channels through which inequality influences growth. We contribute to the literature by using a system of recursive equations, following a control function approach, to empirically assess the relevance of these channels and to differentiate between two forms of inequality. In a single model, we capture both a negative and a positive effect of inequality on long-run economic growth.
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The most used measures are the Gini coefficients and the Theil indices. Some authors have also worked with shares and ratios of the percentiles along the whole distribution of income. On one side, the percentage of the third quartile has been of particular interest to capture the weight of the middle class on the basis that having a strong middle class boosts economic development (Easterly 2001; Partridge 2005). On the other side, the use of different percentile ratios allows for a focus on differentiated effects depending on the specific distributional forms of income (Voitchovsky 2005).
In a similar vein, Davis and Hopkins (2011) argue that panel techniques are not very informative about the relationship between inequality and long-run economic growth.
Ferreira (1999) presents “a brief overview to theories of growth and distribution”, including a review of three mechanisms that give rise to an effect of distribution on growth: political economy channels, capital market imperfections, and social conflict channels. More recently, Ehrhart (2009) and Galor (2009) present a short, though exhaustive and comprehensive, overview of the theories and empirical evidence of the relationship between inequality and economic development. Neves and Silva (2013) provide a critical survey of the empirical literature to explain the sources of conflicting results.
Barro (2000) provides a good explanation of how some approaches can predict a negative or a positive effect on growth, depending on the circumstances. As Barro notes, even under the sociopolitical instability approach, lower inequality may not lead to higher growth: if economic resources are required for the poor to effectively threaten sociopolitical stability, then income-equalising transfers promote stability only to the extent that that they do not encourage the poor to involve themselves in disruptive actions rather than work.
Saint-Paul and Verdier (1996) challenge the conventional political economy approach and argue that, in fact, unequal societies redistribute less and that this in turn is detrimental to growth. More recently, Woo (2011) has suggested a fiscal volatility channel for inequality to negatively influence growth.
Even controlling for fertility, Barro finds a negative effect of inequality in poor countries and a positive effect in rich countries.
In particular, in early stages of development, when physical capital accumulation is the prime engine for growth, inequality can enhance the process of development by channelling resources towards individuals whose marginal propensity to save is higher, allowing for higher levels of investment. In later stages of development, when human capital accumulation becomes the prime engine for growth, and given credit constraints, increased inequality leads to a lower spread of education among individuals, handicapping the process of development due to diminishing returns of human capital. Finally, as capital markets develop and credit constraints are relaxed, inequality becomes irrelevant.
Voitchovsky (2005) does find parallel positive and negative effects in a single model by using different parts of the income distribution. Inequality at the top end of the distribution is positively associated with growth, while inequality lower on the distribution is negatively related to subsequent growth. However, the paper acknowledges that its empirical analysis “is not very informative regarding the different channels through which inequality might affect income.”
Long-run growth is a function of more than just initial conditions. Exogenous shocks will surely play a role in economic growth. But, as long as these shocks are truly exogenous, this type of econometric approach should be consistent. We acknowledge an anonymous referee for this comment.
The use of residual variation in recursive estimation to disentangle opposing dynamics has already been used in the macroeconomics literature. As far as we know, however, it is the first time it has been used for inequality.
We acknowledge an anonymous referee for raising this argument.
Out of 67 possible explanatory variables, Sala-i-Martin et al. (2004) found 18 that were significantly related to long-run growth during 1960–1996. Results suggest that main determinants for growth include initial levels of per capita GDP—the neoclassical idea of conditional convergence—and variables for natural resource endowments, physical and human capital accumulation, macroeconomic stability. Productive specialisation also seems to negatively affect growth, with a negative and significant effect found for the fraction of primary exports in total exports.
These coefficients are adjusted from the WIID database for different possible objects of measure and related to households or families and for the entire population, allowing us to address concerns about international comparability of inequality data. We have previously used these adjusted coefficients, as have other authors (e.g. Atkinson and Brandolini 2010). We rely on income, rather than land or wealth inequality, because income distribution possibly reflects two sources of inequality, namely inequality of opportunities and inequality of returns, which influence economic growth in opposite directions (Neves and Silva 2013).
The selected countries are those for which reliable data for all variables used here have been found. The sample includes major countries from all world regions.
As in Easterly (2007), the considered geographical determinants appear to be highly correlated with institutional variables. However, introducing institutional variables directly is challenging given that institutions are expected to be endogenous to economic performance. More recently, Galor and Özak (2015) use soil types to build a Caloric Suitability Index that they use to predict long-run economic development.
We also consider several other variables for social unrest and violence as robustness checks in the estimations described in Sect. 4. Aside from social unrest and violence, other authors consider variables related to liberties, rights, and institutions. However, data for these variables are only available from the 1980s and economic performance is expected to affect them. We therefore restrict our analysis to the selected variables, which are some of the most commonly used in the literature and help to reduce endogeneity.
When we regress inequality on our controls, fertility rates do not add significant explanatory power, and their use as a valid instrument for inequality is rejected by the instrument tests implemented.
See Terza et al. (2008) for a good explanation of 2SRI and the requisites or its consistency.
We test for the relevance and validity of our approach in different ways. For relevance, we look at the F statistic and the Partial-R-squared of the first regression and perform under-identification tests. For validity, we perform tests of over-identifying restrictions.
We test for the endogeneity of inequality. While Durbin and DWH tests reject the null hypothesis of no endogeneity, the Wooldridge test, which considers robust standard errors, does not (but with a p value of 0.12 comes close to suggesting endogeneity).
We acknowledge the potential impact of multicollinearity in some of the results. In the PE and the CMI channels, the partial R square in the first-stage regression (shown in Table 4) is relatively low, what results in high multicollinearity in the second stage. In any case, these two channels report low estimated coefficients, and consequently non-significance does not seem to be the result of multicollinearity. The other problematic channel, Fertility, on the contrary, reports a high point estimate (−0.037 for inequality and 0.028 for the residual). However, the standard errors are also high. In this case, multicollinearity may be driving non-significant results.
Indeed, the previously studied correlations of our two components of inequality with growth and capital accumulation become stronger if we consider developing and the developed countries separately.
In particular, we test parameter heterogeneity for the coefficients for our two components of inequality based on the OECD-non-OECD dichotomy.
Thus, we expect to partly control for heterogeneity across countries.
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We thank Raul Ramos, Gustavo Marrero, and Paula Herrera-Idárraga for valuable comments. We are also grateful for comments received at the XVI Spanish Applied Economics Meeting in Granada, and at the UBEconomics seminars-2015. We acknowledge the support of ECO2013-41022R.
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Castells-Quintana, D., Royuela, V. Tracking positive and negative effects of inequality on long-run growth. Empir Econ 53, 1349–1378 (2017). https://doi.org/10.1007/s00181-016-1197-y
- Economic growth
- Control function approach