The Palestinian Economy. Theoretical and Practical Challenges

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Abu-Zaineh – Mataria
elaborated decomposition method was advanced to disentangle inequality of health, as
captured by the CI, using a linear arrangement of factor components (Wagstaff, van
Doorslaer et al. 2003). The proposed decomposition allows HI to be both measured and
explained in a convenient way. The method involves disentangling the overall inequality
into a set of CI’s that can be associated with a selected number of explanatory variables.
The above is approximated through an explanatory model specified as a single-linear
equation model that is estimated using a standard OLS regression technique. The
procedure culminates in a decomposition of observed inequality into two main
components, reflecting the part of inequality attributed to differences in need for health
care, and hence, deemed “justifiable”, and the part due to ‘other’ non-need characteristics
(or policy-relevant variables), and hence, deemed “unjustifiable.
Although more illuminating than the aggregate (summary) measures; e.g., HI WV , the
decomposition method – as currently employed – may reveal incomplete and suffer from
several limitations. Firstly, the linear character of decomposition is far from being
consistent with the peculiar nature of health care use data, commonly in the form of
number of visits (integer or discrete variable with skewed distribution – both implies
intrinsically the use of non-linear models. Indeed, it has been shown (van Doorslaer,
Koolman et al. 2004) that while it is practically feasible to use non-linear specifications,
the nature of proposed decomposition necessitates a re-linearisation of the model through
approximation, which, in turn, introduce a bias due to approximation errors. Secondly,
complication may also arise because of the single-equation model upon which the
proposed decomposition is advanced. The probability and count interpretations of data on
health care use may better be specified by a model of two-equation (e.g. a TPM). Indeed,
while the latter specification is shown (Green 2000; Jones 2000) to be more appropriate
and enables estimating the total (unconditional) amount of use within a single-model, the
above decomposition can only be performed in terms of single-equation of the model;
i.e., by decomposing separately the probability of use (estimated by probit or logit), the
conditional use (estimated by OLS or GLM) and the unconditional amount of use
(estimated by zero-inflated or generalized negbin models). Such a practice may raise
serious concerns about the robustness of the decomposable results. Given the variant
modelling used in each step of analysis and the re-linearisation imposed for each of
which, there would be no guarantee of the coherence of the results, nor would it be
possible to ensure that the fraction of inequity due to a certain factor can be partitioned
into a part due to participation behaviours and another due to conditional consumption
behaviours (Huber 2006). Thirdly, the proposed decomposition involves estimates over