The paper uses a new and improved comprehensive dataset on inequality to examine the effects of inequality on per capita income and the effects of per capita income on income inequality. The use of such a comprehensive cross-state panel allows for the estimation of the dynamic responses of inequality and per capita income using panel vector autoregressive (VAR) models. Cumulative impulse responses from a baseline bivariate VAR model indicate that shocks to the Gini index of inequality significantly decrease the level of per capita income. This finding is robust to changes in the measures of inequality used, as well as to the estimation of a three-variable model. We also find that the relationship between inequality and per capita income varies over time and is sensitive to particular episodes in history.
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For example, Forbes states that “if more unequal countries tend to underreport their inequality statistics and also tend to grow more slowly than comparable countries with lower levels of inequality, this could generate a negative bias in cross-country estimates of the impact of inequality on growth”.
We do not plot individual state’s income growth rates or inequality because these graphs are not original, and can be found in Frank (2009b, p. 59).
Note that for \(t=T\), the helmert procedure cannot be done because there are no data to calculate the forward means for \(t>T\).
Love and Zichino (2006) provide an example of this technique being used in firm-level data. She has graciously provided the code for the estimation of the panel VAR.
The use of state-level data limits the number of variables we can include in the model. Furthermore, data limitations have this estimation end in 2000 not 2005 as it does in the baseline version.
Some researchers may argue that if models that emphasize the role of education are right, inequality should not affect income once education is included in the VAR system. What our finding suggests is that education does not completely explain income, hence the reason inequality still affects income (even though the response is now delayed and less severe as expected).
The impact response of the theil index differs possibly because unlike the other indices, the theil is an entropy index. The Theil index, by construction (unlike the Gini and other inequality measures), is most sensitive to inequality in the top range in the distribution (Kovacevic 2010).
We only construct and present impulse responses for the effect of initial inequality on per capita income. Examining the response of initial inequality on average per capita income is meaningless. For example, it is absurd to examine how inequality in 1990 is affected by the average per capita income from 1990 to 2000.
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Atems, B., Jones, J. Income inequality and economic growth: a panel VAR approach. Empir Econ 48, 1541–1561 (2015). https://doi.org/10.1007/s00181-014-0841-7
- Panel vector autoregressions