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

variable, Alesina and Rodrik (1994) or Deininger and Squire (1998) measure
inequality through the Gini coefficient, Persson and Tabellini (1994) use the
percentage of income of the third quintile and Perotti (1996) includes the share
of the third and fourth quintile. The remaining variables also differ from one
study to the other. The initial level of education sometimes is measured as a
flow through the enrollment rates and in other occasions as a stock through
the average years of schooling. Other controls include dummies for democratic
countries, the black market premium or the PPP value of the investment deflator
relative to the United States. A general specification that represents these
estimated models could be written as follows:
(ln y i,t − ln y i,t−τ )/τ = β ln y i,t−τ + γInequality i,t−τ + X i,t−τ δ + µ it (1)
where y i,t is the real GDP per capita in country i measured at year t, 5 τ is the
number of years of the whole period, Inequality i,t−τ measures income inequality
in country i at the beginning of the period- usually in 1960, X i,t−τ is a matrix
including k explanatory variables, µ i istheerrortermandβ, γ and δ represent
the parameters of interest that are estimated .
Despite using different explanatory variables, all these studies obtained a
negative and statistically significant coefficient for the income inequality variable.
This suggests that, other things equal, those countries with higher inequality
in the distribution of income in 1960 experienced, on average, lower
per capita income growth rates during the period 1960-1985.
One of the main criticisms of this kind of regressions is that cross-section
estimators may be biased due to omitted variables in the model. In particular
these regressions fail to control for tastes, the level of technology, resource
endowments, climate, institutions or any other variable specific toeverycountry
that may be an important determinant of the growth rates and may be
correlated with the explanatory variables included in the estimated equation.
Measuring these variables is troublesome because sometimes they are unobservable.
However, if these variables are constant over time we can control for them
including a country specific effect in the model. To do so we could detach the
error term in (1) into three different components:
µ it = ξ t + α i + ε it (2)
where ξ t is a time specific effect, α i stands for specific characteristics of every
country that are constant over time and ε it collects the error term that varies
across countries and across time. Using (2) we could rewrite (1) as follows:
5 In most studies the initial level of income in the set of regressors is included without logs
and others like Barro (2000) also include the log of per capita income squared to pick up a
non linear convergence effect.
8