The Palestinian Economy. Theoretical and Practical Challenges

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Abu-Zaineh – Mataria
the probability of contact) and a generalised linear model (GLM) with a log link and a
zero-truncated negative binomial distribution for the number of visits contingent on
participation (Pohlmeier and Ulrich 1995). The choice of log link is motivated by the
observation that the non-zero values for y i are highly skewed (the skewness varies
between 5.30 and 16.54 depending the level of care used). The log transformation can
thus help lessen the degree of skewness observed in a distribution (Dormont, Grignonc
et al. 2006).
In addition, since our dataset is characterised by a relatively high-dispersion – i.e.,
the variance of the dependent variable is greater than its expectancy – a Poisson
distribution, which has a variance equal to its mean, is not suitable in such context. We
have, therefore, used a zero-truncated negative binomial distribution, which was shown
(e.g., Grogger and Carson 1991) to have more appropriate characteristics. Like others
(e.g., Huber 2006), our explanatory variables to be included in the analysis are selected
based on their significance-level – global nullity test – in a regression explaining the total
number of visits. We have, thus, chosen to select comparable variables for all levels of
health care to facilitate the comparison. Among the selected variables are those whose
exogeneity might be questionable; e.g., morbidity indicators and insurance coverage. For
instance, certain types of morbidity are likely to be altered by the utilisation of health care
(Dormont, Grignonc et al. 2006). Similarly, while the inclusion of a dummy variable
indicating coverage by insurance allows estimating the insurance-effect, the latter may
not be exogenous, given that some (in particular the purchase of voluntary insurance)
may result from individual’s decision, which is related to the likelihood of future
consumption. Although the main purpose of this exercise is to ascertain to what extent the
unequal distribution of such coverage affects the degree of inequity, we have chosen to
include only exogenous regressors to reduce the risk of bias due to endogeneity. An
exogeneity test, following the methodology of Rivers and Vuong (1988), has been
performed to select the variables that were proved to be exogenous. Since the
implemented test enables examining exogeneity of all variables incorporated in the
analysis, the risk of omitted variable bias is not a concern (Huber 2006).
The model is estimated for three levels of health care: primary, secondary, and
tertiary. The regression-estimates are, then, used to simulate various distributions of
health care, and to apply the full decomposition analysis, as described in Section 3.2, to
each level. The CI of each simulated-distribution is estimated using the convenient
(weighted) covariance method, which allows taking into account the sampling weight of
each individual. The weighted covariance between the health care variable (y i ) and the