The 1995/1996 Household Income, Expenditure - (PDF, 101 mb ...

111.14
number of poor households would remain the same. But the PI measure
would change because the size of the shortfall would have increased.
Finally, the P 2 measure indicates the severity of poverty. P, is
constructed by squaring the expenditure shortfalls, so it weighs poor
households with the largest expenditure shortfalls most heavily. If one
poor household gave another poor household half its expenditures, the
Pi measure would not change, but the P 2 measure would change because
tlie donor's new shortfall would be squared. The P2 measure will be
higher in poor populations whose expenditures vary widely from the
poverty line. Policy efforts to redistribute resources among the poorest
of the poor population will most affect the P 2 measure.
Table II!.1 presents expenditure poverty rankings for each of the
three poverty measures by urban or rural region. The rankings run
from richest to poorest, with "Upper Rural" having the highest
incidence, depth, and severity of poverty. The rural areas have the
highest poverty rates in both surveys. The "Other Urban" region ­
Port Said and Suez - has displaced Cairo as the richest area in
1995/1996. In all cases, the null hypothesis that the estimate of Po
equals 0 is rejected at the 99% level of significance. Standard errors of
the other Pa measures are relatively large. The statistical significance
of the difference in any two point estimates can be calculated using
equation (7) of the Statistical Appendix.
The row labeled "All Egypt" gives the simple column sum of the
three measures. Given the assumptions and adjustments above to create
the rural and urban poverty lines, which in standard statistical practice
are the minimum reasonable assumptions and adjustments, the incidence
of poverty in the entire country was 20.69% in 1990/1991, and 44.29% in
1995/1996. The poverty gap in the country almost exactly doubled, from
3.44% to 6.86% over the period. Other sources support these overall
results: nominal per capita expenditures are only 21% higher over the 5
years, yet the population has grown by about 7 million; preliminary
findings from the World Bank's Integrated Survey suggest a similar
deterioration. The more important finding is that, except for the
Frontier region, the rankings are relatively stable over the two periods.
The fact that the classifications are robust over time provides more
information from the standpoint of poverty targeting than the crude
level poverty measures.
Let us assume that the government would like to fill the poverty
gap in Upper Rural Egypt (.1260) for only those households surveyed in
the 1995/1996 HIECS. From Table III.l, the amount of money required to
fill that gap would be
= PI(Z)(PO)(target population)
= (.1260) x (5833.24 £E) x (.6747) x 3504
= 1,737,620 £E.
That figure would have to be multiplied by the sample weight of the
households in Upper Rural Egypt to target the entire area. Of course,
such a policy would assume absolute knowledge of which households to
target, and more, would only be the minimum cost to the government.
In practice, targeted amounts typically fall somewhere between the
perfect-information amount and a crude maximum amount approaching Z
times the entire population.