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Long-term determinants of income inequality: evidence from panel data over 1870–2016

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Abstract

We investigate the major factors which drive income inequality in the OECD countries, using long panel data which span the period from 1870 to 2016. We consider two measures of inequality: the Gini coefficient and the income share of the top 10% of the population. Employing the panel vector auto-regression method, we show that a positive shock to the real interest rate and government spending is negatively and significantly associated with income inequality in the middle, as well as at the top end of the income distribution. An increase in real GDP per capita leads to an increase in income inequality, measured by the Gini coefficient, whereas an advance in financial development reduces it. We find that income inequality responds negatively to positive innovation shocks initially, but this effect becomes positive with some time lag for top-income inequality. Educational attainment significantly reduces top-income inequality. Our results are robust to alternative specifications, including the local projection method and estimations based on different samples. We also capture the time dynamics in our series using a time-varying nonparametric panel data model and show that the real interest rate and financial development are negatively associated with income inequality for most of the period in the post-World War II era, while the effect of real GDP per capita is positive and significant over the same period.

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Notes

  1. Other models such as global VARs (GVARs) are very useful to study the aggregated impacts or on spill-overs of shocks from a large economy. However, as large-scale factor models and due to the issue of the curse of dimensionality, GVARs are restrictive, as they impose a specific structure on inter-dependencies using some weights for aggregating the foreign component of the shocks. For example, GVARs can be estimated by imposing restrictions on the coefficient matrix such that only cross-country averages (assuming N is sufficiently large) enter the PVAR model (see e.g. Dees et al. 2007). As we have a small sample N \((N=17)\), we employ PVAR models which allow focusing on the effects of the various shocks on income inequality in each country and variable in the structural analysis with fewer restrictions.

  2. Further, we make a sensitivity test regarding the identification based on Cholesky decomposition by res-estimating our PVAR model with different orderings of the variables. We find that the results are invariant to alternative orderings. Moreover, the p-value of the Hansen J test for overidentifying restriction obtained from the PVAR estimation is 0, suggesting that we cannot reject the null hypothesis that the exclusion restriction is valid.

  3. Because the time-varying nonparametric technique requires balanced panel data, our sample is restricted to be from 1960 onward.

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Authors and Affiliations

  1. Department of Economics, Monash Business School, Clayton, Australia

    Abebe Hailemariam, Tutsirai Sakutukwa & Ratbek Dzhumashev

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  1. Abebe Hailemariam
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  2. Tutsirai Sakutukwa
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  3. Ratbek Dzhumashev
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Correspondence to Ratbek Dzhumashev.

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Appendix A

Appendix A

1.1 Impulse responses from local projection method

This appendix presents the impulse responses of top income inequality and Gini coefficient, estimated from the local projection method proposed by Jordà, (2005). Figures 8 and 9 show the response of income inequality measured in income share of top 10% and the Gini coefficient, respectively.

Fig. 8
figure 8

Impulse response of top income inequality from a local projection method

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Fig. 9
figure 9

Impulse response of the Gini coefficient from a local projection method

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Hailemariam, A., Sakutukwa, T. & Dzhumashev, R. Long-term determinants of income inequality: evidence from panel data over 1870–2016. Empir Econ 61, 1935–1958 (2021). https://doi.org/10.1007/s00181-020-01956-7

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