the Gini coefficient and the share of the poorest 40 percent of the population
as dependent variables, we tested the following hypotheses:
The Kuznets Curve does not exist. That is,
the level of per capita income has no effect on income distribution, once other
relevant factors are taken into account.
Even if the Kuznets Curve exists, the relationship between per capita
income and income distribution is not stable over time.
Differences in socio-political systems are
much more important than per capita income in explaining cross-country
variations in income distribution. It will be more egalitarian in countries that
are Communist, or suffer extensive government intervention in the economy, or
have no dualistic socio-political structure.
Spread of education leads to greater income
Rate of growth does not affect income
Structure of the economy, especially the relative importance of
primary and manufactured exports, is a major
factor in income distribution.
(7) There are no systematic differences in income distribution among the major regions of the world, once such explanatory variables as socio-political systems or education are included in the analysis.
DATA AND VARIABLES
coefficients of income distribution data were found for 83 countries. For 39
countries, observations for more than one year were available, resulting in 145
observations in total. For the share of the poorest 40 percent, the respective
figures are 80 countries and 136 observations (see Appendix for list of
countries). The data span the post-World War II years from 1952 to 1976, but are
concentrated in the period from 1955 to 1971. Since the Kuznets Curve describes
changes over time, it is reasonable to use several observations from a single
country whenever income distribution data are available for several years. The
basic source is Jain
, supplemented by others listed in the Appendix.
distribution data are notoriously unreliable. The data used here, drawn from a
variety of sources, suffer from all the defects common to the breed. However, we
have statistically tested the influence of outliers on the results (see the
Methodology section below) and found only two sets of outlier data which have
much influence (Taiwan and Pakistan) and only one which seems implausible. The
share of the poorest 40 percent for Pakistan is quite inconsistent with
Pakistan's Gini coefficient and even more inconsistent with the shares
reported for neighboring countries with similar characteristics and per capita
income. Indeed, Pakistan's share is double that of comparable countries.3
Given our doubts about these particular figures, regressions in this
paper for the share of the poorest 40 percent exclude Pakistan.4
Note that if we had included Pakistan's share data our hypothesis on the Kuznets
Curve would have been more strongly supported - indeed the Curve would have
completely disappeared. That the results including Pakistan are quite
inconsistent for the Gini and for shares suggests that shares data on Pakistan
represent a "bad"' outlier.
of Taiwan reverses the signs of the purely intertemporal Kuznets Curve (Table 1)
but in the case of the combined cross-country / inter-temporal curve, it only
weakens the Kuznets Curve effect. Moreover, while Taiwan is an outlier, the
underlying data are plausible. They show a sharp improvement in income
distribution as per capita income rose, but that is precisely what historical
studies of Taiwan's experience have also shown. Therefore it seems reasonable to
include Taiwanese data in the analysis. Results excluding Taiwan are available
from the authors.
and their influence on the results are discussed further below.
For income distribution data, different sources use different definitions and
differ in the populations covered, e.g. the whole country, rural or urban areas;
population, households, income recipients or the economically active. Such
differences in definition or coverage could influence the results. Ideally,
separate regressions should be run for each definition, but there are not enough
observations for some definitions. Moreover, the results of such independent
regressions would hardly be comparable. We therefore made the simplifying
assumption - not implausible in our view - that the differences in definitions
and coverage of income distribution data influence only the intercepts, not the
slopes, of regression curves. This means, for instance, that while we allow for
differences in inequality between rural and urban areas, we assume that this
difference is identical at various levels of per capita income, or for different
levels of education etc.5
The assumption allowed us to reduce possible bias from ignoring
definitional differences, by introducing a set of corrective dummy variables.
The coefficients and t-statistics for these definitional variables are quite
stable and are not of great interest. They are therefore not reported.6
variables are the Gini coefficient and the share of the poorest 40 percent, as
measures of income inequality. Alternative indexes were chosen because interest
in income distribution has focused on both the shape of distribution and the
absolute income and income share of the poor. The main explanatory variable was
per capita income in the 1964 U.S. dollars. The Kuznets Curve is defined as the
quadratic function of the log of per capita income, perfectly standard for
studies of the Kuznets Curve.7
time variables were introduced to capture any shift in the curve. The
interaction of the time variable with the log of income and with the square of
the log of income was to capture any changes in the slope (flattening) of the
variables distinguished the Communist countries of Eastern Europe and countries
with a dualistic socio-political structure. To be defined as dualistic, the
elite had to be a minority and ethnically different from the majority of the
population. Iran is not classed as socio-politically dualistic, although the
economy is dualistic, because the elite is indigenous, but Gabon is, because of
the foreign (French) role in the society and economy for the year concerned.
Judgements may differ on the classification of some countries (see Appendix for
extent of government intervention in non-communist countries is measured by
the share of public investment in total investment. We considered this a more
suitable index than stated ideology since some governments call themselves
socialistic but rely heavily on the market and private enterprise, while a few
proclaim their devotion to private enterprise but intervene massively in the
economy. This index is flawed, since government can intervene as effectively by
controlling private actions as by expanding the size of the government sector.
But no index exists to measure the extent and effectiveness of controls. Other
proxies are even more flawed than the one we used. For instance, the share of
government expenditures in GNP is dominated in some countries by the size of
military expenditures. By that measure, for instance, the U.S. appears far more
interventionist than Japan, contrary to reality. The share of public in total
investment also seems to be broadly correlated with the degree of control over
the private economy. We therefore considered it the most suitable quantitative
was measured by the proportion of children in primary and secondary school,
combined into a weighted index, the same variable used for other studies. To
take account of lags we have used participation rates for roughly five years
before the year of the income distribution data.
test the effect of economic structure, the share of primarily and manufactured
exports in national income seemed more appropriate than the share of primary or
manufactured exports in total exports. One would expect little effect on income
distribution if total exports are 5 percent of GNP, even if the share of primary
or manufactured exports is 90 percent of total exports.
growth was measured by the mean rate of growth in GDP for the five years
preceding the year for which income distribution data are available, to take
account of inevitable lags in the effects of growth-enhancing policies on income
regional (dummy) variables presumably stand for a variety of not clearly defined
historical, social, political and economic factors which groups of countries
have in common, we attempted to define regions that were not only contiguous,
but also showed some other attributes. So, for instance, North Africa was
combined with West Asia on the assumption that shared ethnicity, religion,
history and social characteristics were more important than geographic
definition. The reference region included Western Europe and the developed areas
of European settlement (North America, Australia and New Zealand).
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