Equality, Participation, Transition: Essays in Honour of Branko Horvat

Branko Milanović 55
ascending order according to INCOME; each country has an equal
weight.
All the coefficients (see Table 4.2) have the predicted sign and are
statistically significant at 1 per cent level. 12 The intercept is not statistically
significantly different from zero. This means that, for a sufficiently
low per capita income (at the limit for INCOME0) and in the absence
of state sector employment and transfers, the Gini coefficient would be
close to zero: that is, no inequality would exist. The adjusted coefficient
of determination is 0.70. 13 The interpretation of the results is as follows.
Each ten percentage point increase in the share of state-sector
workers reduces inequality, on average, by 2.2 Gini points; each
increase in social transfers by 10 GDP percentage points lowers
inequality by 4.15 Gini points. The relationship between income level
and inequality is quadratic: at first, inequality rises with income and
then declines. The turning point is reached for $2100 per capita (at
1988 international prices) which is broadly the level of income of the
Philippines, Swaziland, or Sri Lanka. 14
There is a heteroskedasticity problem with equation (1.0). It was
observed in the literature (see Lindert and Williamson, 1985: 344;
Lecaillon et al., 1984: 40) that the dispersion of the Gini is greater
at low than at high income levels. One can therefore expect some
heteroskedasticity because standard errors would systematically decline
with increase in income level. Indeed, this is exactly the case, as shown
in Figure 4.2, where residuals from equation (1.0) are plotted against
income levels. Regression (1) is the same as (1.0) except that I correct
for heteroskedasticity by running OLS with White’s heteroskedasticconsistent
standard errors. 15 Values of the coefficients do not change.
Significance of STATE or TRANS is also unaffected but the two income
terms become statistically significant at 5 per cent level rather than at 1
per cent as in regression 1.0. Since the same problem exists in all equations,
all regressions will henceforth be run with the correction for
heteroskedasticity. I shall explore next whether these results are sufficiently
robust to a different specification of each of the variables used
here. I shall start with the dependent variable, a measure of inequality.
16 As explained in the Annex, I have collected the income Gini
coefficients for 80 countries for the period of the 1980s with a specific
purpose to use it for this study (I call it, for simplicity, even if somewhat
self-servingly, the Milanović data set). 17 Since inequality measures
are notoriously fickle, one can wonder if the same results would
obtain with inequality measures coming from a different source. To
do this, I use the Deininger and Squire (1996) ‘high-quality’ data set