The Palestinian Economy. Theoretical and Practical Challenges

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Proceedings “The Palestinian Economy: Theoretical and Practical Challenges” 377 equal to its means), to one in which this variable is allowed to vary across income, keeping all else constant. Therefore, the contribution of the variable (x k ), as measured by the C k and expressed in percentage terms of the overall measured inequity (the HI), may be interpreted as in (van Doorslaer and Koolman 2004): “income-related inequity in health care utilisation would, ceteris paribus, be X% lower, if variable x were equally distributed across income range – or if in C ŷ due to x k were equal to zero”. Table 4.1 presents the partial contribution of six sources of inequalities attributed to distribution of socioeconomic variables 3 and Figure 4.2 (a-b) visualise the contribution of each of which to the total inequality. A closer look at these variables reveals that eventually all show positive and significant role in generating inequality in the utilisation of various levels of health care. However, the most influential variable is the (log of) household income: the partial contributions of income are always significant and sizeable – between 0.0141 in case of primary-care and 0.0961 in case of tertiary-care, but in all cases, they account for about 40% of the HI values – being significantly more important in the case of tertiary-care [circa 45 % of the HI]. Basically, this means that, ceteris paribus, the pro-rich inequity in the utilisation of each levels of health care would be about 40% [45% in the case of tertiary-care] lower than that observed, if income were distributed equally. Quite interestingly, with the exception of primary-care, the pro-rich contributions of income are found alone sufficient to counterbalance the pro-poor inequality from the distribution of need (note that the pro-rich contributions of income in these cases are fairly higher than that of need), but not sufficiently so to offset the “very pro-poor” distribution of need in the case of primary-care. Despite the importance of income contribution in generating the measured inequity in the utilisation of all levels of care, the observed discrepancies between the HI and the income contribution to inequity suggest that other socioeconomic characteristics (factors) play also an important role in generating inequity. Indeed, apart from income itself, Table 4.2 shows that “other” important variables contributing to pro-rich distribution of care utilisation are education attainment, insurance coverage, activity and marital status, and urban residency. In all cases and in the two regions, education variables emerge to be invariably the second source of the generated inequity with a (partial) pro-rich contribution being in the range [0.0041; 0.0172]. The partial contributions of education to 3 Note that in the case of categorical (dummy) variables such as education, activity and marital status, this still represents the (aggregated) contribution of the respective variables in the category. The subdecomposition showing each single variable’s contribution is not presented here in order to simplify the presentation and interpretation of the decomposition.
378 Abu-Zaineh – Mataria the HI appear to be relatively slightly more important in the case of primary- (circa 12%) and secondary-level (13 %) compared to tertiary-level (about 8%). Similarly, for almost all levels of health care and in the two regions, the dummy variable indicating any coverage by insurance appears to be a significantly positive contributor to the pro-rich distribution of care utilisation in the range [0.0004; 0.0152; p < 0.05]. However, the extent to which differences in insurance coverage by income contributes to inequity appears to vary significantly by the levels of care, but not between the two regions: they appear to be relatively a lot more important in the case of tertiary-care [7% of the HI] compared to secondary-care [circa 3% and 2% of the HI for inpatient and outpatient, respectively], whereas they appear to be less important contributor for primary-care [1% of the HI]. Yet, in the latter case, the pro-rich contribution of insurance appears only significant [at p < 0.05] in the case of WB, but not GS. The partial contributions of activity status and marital status to the measured degree of inequity are generally more important than the insurance coverage per se. However, once again, some variations in the extent to which these two factors drive inequity in the utilisation of the three levels of care emerge: the relative contributions of activity and marital status are larger and significant [at p < 0.05] for the case of primarycare [circa 7% of the HI] and secondary-inpatient care [circa 5% and 4% for activity status and marital status, respectively] compared to secondary-outpatient [circa 2% of the HI, and both are only significant in the case WB at p < 0.05], whereas they appear to be trivial for tertiary-care [less than 0.5%], with the partial contribution of marital status being insignificant at p < 0.05. Lastly, the contributions of the dummy variable indicating (urban) residency appear to play an important role in generating the measured degree of inequity for the three levels of care and in the two regions. However, the differences in the relative importance of such factor between the two regions and across the levels of care are equally noteworthy: while there are some substantial differences between the two regions – for all levels of care, urban residency in the WB contribute roughly twice as much as the urban residency in GS – their partial contributions are particularly more important in the case of tertiary-care [10% and 5% of the HI in the WB and GS, respectively] and secondary-care [8.4% and 5.2% for outpatient case and 7% and 5% for inpatient-case in the WB and GS, respectively] compared to primary-care [where they only constitute about 2.4% and 1.4% of the HI in the WB and GS, respectively], and appear to be insignificant at p < 0.05 in the case of GS. To sum up, the (aggregate) contribution to the measured pro-rich inequity, which is not due income per se constitute about 30% of the total measured inequity (HI) index
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The Palestinian Economy Theoretical
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© Gianni Vaggi - Marco Missaglia -
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VOLUME II
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246 Zagha 1. Introduction The local
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248 Zagha and groups in political d
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250 Zagha drawbacks in the politica
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252 Zagha it is not surprising that
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254 Zagha economy (including expend
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256 Zagha social norms sharply dist
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258 Zagha Bird (1995) puts it, info
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260 Zagha intergovernmental fiscal
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262 Zagha low rate flat tax levied
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264 Zagha need to be given access t
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266 Zagha Such failures are found i
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268 Zagha 4.1.3. The Relationship b
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270 Zagha a final account report of
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272 Zagha 4.1.6. Functions of LGUs
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274 Zagha • Management of LGU ass
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276 Zagha Figure 2: Local Governmen
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278 Zagha In summary, what the data
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280 Zagha on the cash basis. Almost
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282 Zagha Figure 7: Business Opinio
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284 Zagha within the Palestinian na
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286 Zagha Table 5: Number of NGOs i
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288 Zagha Fiscal decentralisation i
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290 Zagha Burki, J., Perry, G., Dil
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292 Zagha Palestinian Legislative C
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Issues, Performance and Trajectorie
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The Effect of the Israeli-Palestini
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428 Olivera 3. Traditional economic
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430 Olivera costs and lower market
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