Amparo Castelló-Climent, Universidad Carlos III de Madrid ... - Ivie

duetoaproblemofweakinstruments. For this reason columns (4) to (6) display
the results of the system GMM estimator. The results show that using
the variables in differences to instrument an additional level equation changes
the estimated coefficients. In particular, the coefficient of the income Gini index
continues being positive although it is not statistically significant at the
standard levels. In addition, the coefficient of the initial per capita income is
reduced significantly and the coefficients of the average years of schooling are
now statistically significant. With regard to education inequality, the human
capital Gini index continues having a negative and statistically significant effect
on growth.
The consistency of the first differences and system GMM estimators depends
on two identifying assumptions. The first one states the absence of second order
serial correlation. The second one regards with the validity of the istruments.
We examine the first assumption testing the hypothesis that the differenced
error term is not second-order serially correlated. The second assumption is
analysed through the Sargan test of over-identifying restrictions, which test the
null hypothesis of validity of the instruments. The p-values shown in Table 4
give support to the identifying assumptions since in all cases we do not reject
the null hypothesis.
Overall, the results suggest that the effect of income inequality on the growth
rates depends to some extent on the technique used to estimate the model.
Whereas cross-section and pool regressions show a negative relationship between
income inequality and growth, controlling for fixed effects displays a positive effect
on the growth rate within a country from an increase in income inequality
in that country. However, the negative relationship between human capital inequality
and economic growth appears to be extremely robust to the estimation
of different models. In particular, the negative effect on growth from human
capital inequality is found not only in cross-section regressions but also in the
estimation of a dynamic panel data model that controls for country-specific
effects.
4 Human capital and economic growth: broader
sample
One of the main criticisms to Forbes’ (2000) study is that the results may suffer
from sample selection bias due to the restrictive number of countries for which
there are data on income inequality. In addition to measurement error problem,
one of the main shortcomings with income inequality data measures is that the
least developed countries are underrepresented. For example, in Forbes’ sample
almost half of the countries are OECD countries and there are no data for
any Sub-Saharan African country. Although we have extended Forbes’ sample
and, therefore, improved in some sense that shortcoming, some regions such us
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