Arab Development Challenges
Background Paper 2011/11
Heba El-Laithy
The ADCR 2011: Poverty in Egypt
(2009)
Heba El Laithy
i
United Nations Development Programme
Arab Development Challenges Report
Background Paper 2011/11
The ADCR 2011:
Poverty in Egypt (2009)
Heba El-Laithy*
Heba El-Laithy is professor of Statistics at Cairo University
E-mail: hflaithy@gmail.com
Comments should be addressed by email to the author
i
Acronyms and Abbreviations
CAPMAS
FHH
GDP
HIECS
ILO
IMF
MHH
SE
UNDP
Central Agency for Public Mobilization and Statistics
Female-headed Household
Gross Domestic Product
Household Income, Expenditure and Consumption Survey
International Labour Organization
International Monetary Fund
Male-headed Household
Standard Deviation
United Nations Development Programme
ii
Introduction
Poverty alleviation has been given high priority on the agendas of international
organizations, governments and civil society organizations in different parts of the globe. In
view of the nexus of growth, employment and poverty alleviation, it has been increasingly
acknowledged worldwide that an effective poverty reduction strategy requires increasing the
access of the poor to productive and decent employment; for although the causes of poverty
worldwide are many, the most important are unemployment, underemployment, informal
employment and low wages. These phenomena are, in turn, the outcome of low rates of
investment leading to slow rates of growth and the misallocation of scarce resources
resulting in jobless growth. As for the supply side of the labour market, policies would need
to be geared toward enhancing the human capital of the poor, through increased social
investment in health and education.
For Egypt, particularly, previous studies on labour market and poverty revealed that the
unemployed are not necessarily the poor or the illiterate, and rather, the overwhelming
majority of the unemployed are certificate holders. Meanwhile the informal sector has been
increasingly growing and absorbing a high percentage of poor workers working informally,
without social protection, with low wages, low productivity, and working under bad
conditions. This is not surprising given that, too often, the poor cannot afford to be without
work and must reconcile with whatever type of employment opportunities are available.
Thus, effective poverty reduction policies must also target under–employment and informal
employment.
Rapid growth in jobs and incomes in the late 1990s, reversing the patterns of the
slowdown in growth since 1987, led to a drop in poverty for the first time since the
early 1980s. Over this period, poverty patterns changed from the urban-rural divide
that had characterized the past to a geographical/regional pattern.1 Moreover this
growth of the late 1990s was obtained through domestic fiscal and monetary
expansion policies that were not fully sustainable. Hence, the budget deficit grew
from 1% to 4% of GDP, and credit to the private sector averaged well over 25%
between 1996 and 1999.
The period 2000 to 2004 was a bottle neck in terms of Egypt’s economic performance. After
the slowdown in the Egyptian economy during the period 2000-2003, certain signs of
recovery in the year 2004, were notable. Furthermore, the currency floatation and resulting
devaluation, starting in January 2003, do not bode well for poverty rates, which are likely to
increase as a consequence in the following years. Since 2000, growth rates have slumped.
A slowdown in domestic stimulus (especially credit) has also slowed the construction
industry, and tourism has fallen drastically after 9/11 and because of instability in the region.
Given that poverty in Egypt is fairly shallow, with much of the poor clustered just below the
poverty line and much of the non-poor just above the poverty line, many of those who
escaped poverty during the 1995-2000 period may have slipped back into poverty during the
successive five years.
During 2005-2008 Egypt experienced high GDP growth, accompanied by high inflation,
which damaged the living standards of the poor. However, until the food crisis hit in late
spring of 2008 the trend of living standards was clearly positive. World Bank 2009
demonstrated that rapid economic growth had [a] strong poverty reduction effect, and
poverty and near-poverty incidence fell during February 2005-February 2008 by around
20%. But these gains have been short-lived due to the food, fuel and financial crises.
1
Measuring Poverty in Egypt
Methodologies for constructing a poverty line for Egypt
The ‘money metric’ measure of poverty usually used by the various researchers is
expenditures, as calculated from the various HIECS 2 , to construct a ‘poverty line’ that
differentiates between those who have an adequate level of welfare and those who do not.
In this report, consumption expenditure is used as the welfare indicator in the estimation of
the poverty line and in making poverty assessments.
For each household in the sample, this paper uses data from the 2005 and 2009 HIECS to
construct its own food poverty line. This line satisfies the particular household’s minimum
nutritional requirements depending on age and gender composition, as well as location.3 Of
course, this also leads to a variation in the appropriate poverty line, depending upon the
location and composition of a particular household.
Thus the ‘household-specific’ methodology used for this report attempts to account for
differences in regional prices, differences in needs of household members as well as
economies of scale. In doing so, the estimated poverty lines ensure that regional differences,
relative prices, as well as differences in size and age composition of households are
accounted for. This results in a rank distribution which is consistent with the chosen indicator
of household welfare.
Poverty measurement and trends, 2005-2009
In 1995/96, poverty stood at 19.4%, declining significantly to 16.7% in 1999/2000. The gains
achieved in reducing poverty from 1995-2000 were offset by the increase in poverty from
2000-2004 back to 19.6%. Finally, as shown in table 1, in 2008-09 overall poverty in Egypt
stood at 21.6%, representing approximately 16.1 million, who could not obtain their basic
food and non-food needs. The poverty gap index also increased from 3.6 to 4.2% (refer to
Notes of table 1 for an explanation on indices).
Table 1: Overall poverty measures, 2005 and 2009
Headcount rate (P0)
Poverty gap (P1)
Squared poverty gap (P2)
2005
2009
Change
2005
2009
Change
2005
2009
Change
Urban
10.1
11.0
0.9
1.8
2.0
0.2
0.5
0.6
0.1
Rural
26.8
28.9
2.1
5.0
5.6
0.6
1.4
1.7
0.3
Total
19.6
21.6
2.0
3.6
4.1
0.5
1.0
1.2
0.2
Source: Authors calculations using HIECS, 2004-05 and 2008-09.
Notes: P0 represents the incidence of poverty, and is calculated using the absolute poverty line. P1 represents the
poverty gap index, measuring the depth of poverty, which captures the percentage of shortfall below the poverty
line for the whole population. P2 represents the severity index of poverty, which accounts not only for the depth of
poverty but also for inequality of income or consumption among the poor,
Table 2: Urban-rural poverty decomposition, 2008-09
Absolute change
Change in poverty (P0)
2.46
Total Intra-sectoral effect
2.01
Population-shift effect
0.39
Interaction effect
0.06
Intra-sectoral effects:
Urban
0.23
Rural
1.78
Percentage change
100
81.8
15.73
2.46
9.19
72.61
Source: ibid
2
At the sub-national level, during the increase in poverty over the period 2000-2004, rural
residents were net losers as poverty incidence; gap of poverty and its severity were higher in
2004. During 2005-2009, both urban and rural areas experienced increases in poverty
incidence, although of different magnitudes (changes were 0.9% in urban areas and 2.1% in
rural areas). Overall change in poverty is due to changes in poverty within urban and rural
areas rather than population shift from rural to urban areas (Table 2).
Figure 1: Growth incidence curve, 2005 and 2009
Source: ibid.
Notes: The horizontal axis shows the expenditure group arranged in 2 percentile increments from poorest to
richest: 1 was the poorest 2% of the region’s population; 49 was the second richest group, with expenditures
th
th
between the 96 and 98 percentiles.
The vertical axis shows growth in expenditures for the particular expenditure group between 2005 and 2009, in%.
The dashed line shows the mean growth in expenditures between 2005 and 2009.
Average per capita expenditure showed a decrease during the period 2000 to 2004 and a
further decrease during 2004-05 to 2008-09 (Table 3). On a national level, the average per
capita expenditure in 2008-09 (at 2004-05 prices) was L.E. 2510 per annum, compared to
L.E. 2529 in 2004-05, pointing to an annual decrease in real average per capita expenditure
between 2005 and 2009 of -0.75%. Despite the overall decrease in per capita expenditure,
the average per capita for the poorest three quintiles of the population experienced
increases, with the highest increase amongst the poorest quintile. Indeed, the decrease in
overall per capita expenditure is driven by losses of the richest two deciles of the population
reflecting improvement in income distribution. However, the substantial increases in food
prices, which outweighed increases in the prices of non-food items, have had significant
negative ramifications on those under the lower poverty line for whom food represented 74%
of their expenditures. Per capita consumption deflated by poverty line had declines by -5.1%
(-1.3% per annum) and by -3.7% (-0.9% per annum) for the poorest quintile, (Figure 1). This
trend explains trends on poverty during the period 2000-2004: poverty increased coupled
with the overall decrease in real per capita expenditure.
Table 3: Mean expenditure for different regions, 2005 and 2009
Region
Urban
Rural
Metropolitan
Lower Urban
lower Rural
Upper Urban
Upper Rural
Total
Source: ibid.
2005
3,298.95
1,940.34
3,983.74
2,731.96
2,115.73
2,803.74
1,719.11
2,529.48
2009
3,315.22
1,949.79
4,000.36
2,955.54
2,172.02
2,646.77
1,679.99
2,510.48
% change
0.49
0.49
0.42
8.18
2.66
-5.60
-2.28
-0.75
3
Trends in Gini coefficients, as measurements of inequality, (Table 4) decreased as well.
Income inequality declined during the period 2000-2005 (from a Gini coefficient of 36% in
2000 to 32% in 2005). This decreased slightly during 2005-2009 to 31%.
Table 4: Inequality in per-capita expenditure distribution by urban and rural areas, 2005 and
2009
Year
Bottom half of the
distribution
p25/p10
p50/p25
2005
2009
1.29
1.28
1.34
1.33
2005
2009
1.32
1.32
1.38
1.38
1.26
1.27
1.3
1.28
2005
2009
Source: ibid.
Upper half of the
distribution
p75/p50
p90/p50
Total
1.39
2.05
1.38
1.99
Urban
1.45
2.26
1.42
2.15
Rural
1.3
1.66
1.28
1.64
Inter quartile
range
p75/p25
p90/p10
1.87
1.84
3.55
3.4
31.85
31.1
2
1.96
4.1
3.92
34.38
33.68
1.68
1.63
2.71
2.65
22.85
22.38
Tails
Gini
Even though poverty seems to be deepening, poverty in Egypt is shallow, meaning that a
large percentage of the poor are clustered just below the poverty line while many of the nonpoor are found just above it. Therefore, any small change in household consumption can
affect poverty and the consequent poverty rates. Moreover, declining income distribution is
often observed during periods of slow economic growth, and in relation to poverty trend
reports in Egypt, World Bank 2007 and 2009 suggests that when real consumption declines,
inequality improves. One explanation for this is that the consumption level of the poor is
already low and there is little room for it to fall any lower, which is what happened in Egypt
during the two periods under consideration.
There are several reasons for the observed trends in poverty rates and income distribution.
First, Egypt experienced positive growth rates in real GDP between 2000 and 2009, but this
growth was coupled with high inflation, especially for goods and services consumed by the
poor. Thus, per capita consumption deflated by the poverty line (as a welfare measure) has
declined, which indicates that macroeconomic achievements have not been successful in
reducing poverty levels. Second, in April 2008, the Egyptian Government responded to price
increases through the expansion of the food subsidy system. Due to the dramatic escalation
in food prices in Egypt and around the world, the Government of Egypt introduced several
measures to redirect benefits towards those who were in most need. These measures
included separating the production and distribution of Balady bread; re-opening the
registration system for newly born children to ensure their inclusion in the ration card system;
removing food items not in demand; increasing quotas to higher, subsidized, rates; the
piloting of the smart card system, and expanding the social assistance coverage. As a result
of these changes, the food subsidy bill increased from LE 16.4 billion in 2007-08 to LE 21.5
billion in 2008-09, and the government incurred a high fiscal cost of 2.1% of GDP in 200809.4
A change in the distribution of income is determined by two factors. First, there is the effect
of a proportional change in all incomes that leaves the distribution of relative income
unchanged, i.e. a growth effect. Second, there is the effect of a change in the distribution of
relative incomes which, by definition, is independent of the mean, i.e. a distributional effect.
The net impact is deterioration in real per capita consumption coupled with improvements in
consumption distribution. Deterioration in per capita consumption increases poverty (growth
effect) while improvement in consumption distribution decreases poverty (distribution effect).
A change in poverty can act as a function of growth, distribution and the change in
distribution.5
4
Changes in poverty are mainly a result of declining mean expenditure; however improved
inequality partially compensated the adverse effect of declining expenditure averages. In fact
if mean expenditure (deflated by the lower poverty line) had not declined, the incidence of
poverty would have been decreased by 1.31%. Conversely, if there were no improvements
in income inequality, poverty would have increased by 3.61%. This is an indication that there
was a decline in per capita expenditure that outweighed the improvement in income
distribution.
Figure 2: Annual growth rates in GDP (%), 2000/01-2008/09
8
6
4
2
2008/2
009
2007/2
008
2006/2
007
2005/2
006
2004/2
005
2003/2
004
2002/2
003
2001/2
002
2000/2
001
0
Source: Ministry of Economic Development, Follow up report of the economic and social plan performance, 20082009
Regional poverty in Egypt, 2005-2009
Overall poverty masks differences in welfare among regions and among governorates within
the respective regions.6 The incidences of poverty are highest in Upper rural regions. In
general, rural areas in all regions have higher poverty measures than their urban
counterparts; with a poverty incidence in rural areas double that of urban areas. Using the
lower poverty line, in 2009, poverty incidence is highest in the Upper Rural region (46.1%),
followed by Upper urban region (21.7%) and is the lowest in the Metropolitan region (6%)
(Figure 3). Differences in poverty measures across regions are thus statistically significant,
and the ranking of regions remains unchanged for other measures of poverty. This indicates
that not only do poor households in the Upper rural region represent large proportions of
their population, but that their expenditure level is far below the poverty line.
Figure 3: The incidence of poverty by region, 2004-05 and 2008-09
2005
50
2009
40
30
20
10
0
Metropolitan
Lower Urban
Lower Rural
Upper Urban
Upper Rural
All Egypt
Source: Authors calculations using HIECS, 2004-05 and 2008-09.
As table 5 shows, the distribution of the poor is quite uneven across regions. Poverty,
particularly extreme poverty, is relatively low in urban areas where 41.1% of the population
resides. In rural areas, poverty is mostly located in the Upper Rural region, which has the
highest contribution to national poverty as previously demonstrated in figure 3. Almost 55.8%
of the poor in Egypt live in the Upper Rural region, yet its share in poverty far exceeds its
population share of 26.6%. Moreover, its share to overall poverty increases with the
distribution sensitive measures, reflecting the low standards of living of the poor in this
region.
5
Table 5: Changes in poverty measurements by region (%), using the lower poverty line, 2005
and 2009
Poverty headcount rate
2005
2009
Change
5.7
6.0
0.3
9.0
6.8
-2.2
16.7
16.6
-0.1
18.6
21.7
3.1
39.1
46.1
7.1
19.6
22.0
2.5
Region
Metropolitan
Lower Urban
lower Rural
Upper Urban
Upper Rural
All Egypt
Source: ibid.
Distribution of the poor
2005
2009 Change
5.4
4.6
-0.8
5.6
3.6
-2.0
26.2
24.0
-2.2
11.3
11.3
0.0
50.6
55.8
5.2
100
100
0
Distribution of population
2005
2009
Change
18.7
17.0
-1.6
12.1
11.5
-0.6
30.7
31.8
1.0
11.9
11.5
-0.4
25.4
26.6
1.3
100
100
0
Thus, in Egypt, where a family lives has a significant correlation with poverty. In addition to
differences in educational levels, job availability, and the availability of public services, roads
and markets, variation in the quality, cropping patterns and land ownership of agricultural
land may contribute to the wealth gap among regions.
A general approach to assessing changes in poverty over time
As is clear from the foregoing analysis, the assessment of poverty changes over time is
essentially arbitrary, depending on, and varying according to the chosen poverty line. We are
not certain whether we would obtain the same conclusions if we used a different poverty line.
The previous comparisons of poverty changes over time are therefore partial rather than
complete. To assess the robustness of the poverty measurements to the poverty lines used,
stochastic dominance analysis is carried out to examine whether or not the same
conclusions are obtained if the poverty line is changed. That is, curves for the three poverty
measures were plotted using a wide range of values for the poverty line (30% to 100% of
average per capita expenditure). These curves were used to rank poverty levels for the
years 2005 and 2009, for a range of poverty lines. The findings are classified at the national
and urban/rural levels.
At the national level, as shown in figure 4, the curves for the headcount index for 2004-05
and 2008-09 do not intersect with each other, as the P0 curve for 2008-09 is consistently
below the P0 curve for2004-05. Thus, for all poverty measures and at any poverty line,
poverty was lower in 2004-05, indicating that regardless of the poverty line chosen, poverty
has increased during the periods 2004-05 and 2008-09. The above pattern of change holds
for curves of the poverty gap index.
Figure 4: The incidence of poverty curves for all Egypt, 2004-05 and 2008-09
Total
Total
1
2005
6
2009
4
.6
Total deficit
Cumulative distribution
.8
.4
2005
2009
2
.2
0
0
0
1.6
3.2
4.8
Welfare indicator, units
6.4
8
0
1.6
3.2
4.8
6.4
8
Welfare indicator, units
Source: ibid.
6
Poverty by governorate, 2008-2009
Regional poverty measures mask significant differences across governorates. The
incidence, depth and severity of poverty vary considerably within each region. Annex table 1
presents poverty measures for various governorates in urban and rural areas respectively.
As seen from the annex table 1, irrespective of the poverty index, the poverty indices of all
governorates in Upper Egypt exceed the corresponding indices at the national level, except
for Luxor governorate. Poverty incidence is highest in the governorate of Assiut, about three
times the national level. Assiut is followed by Sohag and Bani Suef governorates. The same
pattern holds for the poverty gap and severity indices. Assiut, Sohag and Bani Suef have the
largest poverty indices, which are almost five times the national levels.
In Lower Egypt, Behera governorate is the only governorate where poverty measures
exceed the national level. For the Metropolitan governorates, Cairo has the largest poverty
measures. The incidence of poverty in Cairo amounts to 6.3%, ranking the 5th lowest
governorate, with all other poverty indices below the national level. Even though its
contribution to national poverty indices is less than its share in population, it includes 2.9% of
all poor. Figure 5 below summarizes the incidence of poverty across all governorates for
2008-09.
Figure 5: Poverty incidence by governorate, 2008-09
Assiut
Sohag
Bani Suef
Aswan
Qena
Menia
Fayoum
Giza
Beheira
Luxor
Sharkia
Menoufia
Ismailia
Kafr el Sheikh
Qualiobia
Dakahlia
Garbeyya
Cairo
Alexandria
Port Said
Damietta
Suez
0
10
20
30
40
50
60
70
Source: Authors calculations using HIECS, 2008-09.
On the other hand, the elasticity of poverty measures to changes in the mean expenditure
and inequality were estimated. Indeed, the elasticity of poverty measures to the mean
expenditure and to the inequality index were lower (in absolute terms) for rural areas
compared to urban areas (Tables 6 and 7), implying weaker response to growth in
expenditure or improvement in inequality.
Elasticity to consumption change is about 3; thus, for every percentage growth in mean
expenditure, the headcount index would decline by 3.29% in urban areas and by 2.86% in
rural areas. Response to inequality changes is much stronger in urban areas, where
everyone percentage increase in inequality would raise poverty incidence by 4.74%
7
compared to only 1.09% in rural areas. This may explain the changes in poverty between
2004-05 and 2008-09, as described in the previous sections.
Table 6: Elasticity of poverty with respect to consumption, 2005 and 2009
Poverty headcount rate (P0)
Poverty gap (P1)
2009
2005
Change
2009
2005
Change
Urban
-3.29
-3.13
0.16
-3.65
-3.68
-0.04
Rural
-2.86
-2.92
-0.06
-3.36
-3.59
-0.23
Total
-2.95
-2.97
-0.02
-3.41
-3.61
-0.20
Source: Authors calculations using HIECS, 2004-05 and 2008-09.
Region
Squared poverty gap (P2)
2009
2005
Change
-3.93
-4.04
-0.12
-3.69
-4.01
-0.32
-3.73
-4.02
-0.29
Table 7: Elasticity of poverty with respect to inequality, 2005 and 2009
Region
Urban
Rural
Total
Source: ibid.
Poverty headcount rate (P0)
2009
2005
Change
4.74
4.81
0.07
1.09
1.35
0.25
2.53
2.86
0.32
Poverty gap (P1)
2009
2005
Change
6.26
6.55
0.29
2.48
2.87
0.39
4.14
4.65
0.50
Squared poverty gap (P2)
2009
2005
Change
7.28
7.56
0.28
3.59
4.04
0.45
5.33
5.87
0.54
Poverty Profile
Defining the characteristics of the poor in Egypt is an essential first step toward an
appropriate poverty reduction strategy. Low income is not the only feature of poverty.
Poverty is often associated with malnutrition, higher incidence of child mortality and
morbidity, lower education levels, poor housing conditions and/or limited access to basic
services of water and sanitation. The distribution of welfare in Egypt should therefore focus
not only on the actual numbers of the poor, but also on the characteristics of the populations
that fall below a given poverty line. This analysis is of particular value to policy makers
entrusted with the design and targeting of poverty alleviation strategies. The profile of the
poor will be explored here in terms of educational attainment, employment characteristics,
demographic characteristics, household characteristics and income sources.
The poverty profile is a description of poverty focusing on two related yet different questions:
“who is at risk of poverty?” and “who are the poor?” By examining which population groups
face a higher risk of poverty, one can gain insights into the factors associated with poverty
and identify the groups with high incidence of poverty; those are expressed through poverty
measures. But as some of these risk factors only affect a small share of the population, a
group with a high poverty risk does not necessarily account for a large fraction of the poor.
The answer to the second question examines the composition of the poor and shows which
groups are over-represented among the poor; those were expressed through the distribution
of the poor and non-poor among different groups of different characteristics. Both parts of
the poverty profile have important policy implications. The first: “who is at risk?” helps to
reveal the causal factors of poverty and design policy interventions that are most likely to
help the targeted group. The second: “who are the poor?” helps to identify factors and
policies that will likely affect the majority of the poor.
Education and poverty
Education is a powerful shield against poverty. In Egypt, as in most countries across the
world, there is a negative correlation between the risk of poverty and the level of education
of household members as well as the household head. Education determines the command
of individuals over income earning opportunities through access to various types of
employment. Education, as substantiated by several empirical studies, has a high
explanatory power on observed patterns of poverty. The correlation between education and
8
welfare has important implications for policy, particularly for the distributional impact. This
sub-section discusses the educational characteristics of the poor in terms of their
educational attainment.
Educational attainment
Data from HIECS 2008-09 shows that almost a quarter of the population (27.7%) aged 15
years and above in Egypt was illiterate, and 20% had completed their basic education, while
only 9% were university graduates or more (Figure 6.C and table 8). This pattern was more
pronounced for the educational levels of the heads of households (Annex table 4). Figure 6
highlights the gap in educational attainment between urban and rural areas, and between
poor and non-poor households.
Figure 6: Individual educational profile in urban (A), rural (B) and all Egypt (c), 2008-09
Poor
All urban
50
0
0
University
0
Secondary
10
Basic
10
Read and write
10
Illiterate
20
University
30
20
Secondary
30
20
Basic
40
Read and write
40
30
Illiterate
40
Poor
All urban
University
Non poor
Secondary
50
(C)
Non poor
Basic
All urban
Read and write
50
(B)
Poor
Illiterate
(A)
Non poor
Source: Authors calculations using HIECS, 2008-09.
Incidence of illiteracy among individuals in rural areas is 34.2%, compared to only 18.9% in
urban areas. Specifically, illiteracy is more prevalent amongst the poor in rural areas (42.3%)
compared to urban areas (33.5%).urban areas. How is this educational pattern reflected in
the households’ standards of living? It is clear that poverty is inversely correlated with
educational attainment, so that even a moderate improvement in education could reduce the
ranks of the poor. The great majority of the poor (57.7%) was illiterate or could read and
write only with no education certificate, while only 1.9% of the poor had a university
education.
There were significant regional variations in educational attainment and its correlation with
poverty. Urban residents are more likely to have attended school and to have remained in
school for a longer period than rural residents. The ‘Urban’ panel of figure 6 shows that the
profile of the poor significantly biased toward the lower levels of education. Gaps in
educational attainment between the poor and non-poor are larger in urban areas than rural
areas. As indicated by the ‘Urban’ panel of figure 6, the proportion of illiterate poor
individuals is 34%, while illiteracy rate among the non-poor is only 17%.
Perhaps the most striking feature, however, is that the magnitude of the differences between
poverty groups, relative to the magnitude of the urban/rural gaps, are in general much larger.
This is, essentially a manifestation of the strong role of education as a determinant of
poverty. However, the ‘Rural’ panel in figure 6 shows an interesting contrast for rural Egypt –
while there was a higher proportion of illiteracy among the poor (42%), the general profiles of
the poor and non-poor do not differ very much. In other words, education seems to be a
weaker cause of poverty in rural Egypt.
9
Non-poor
Poor
Total
30.63
42.27
34.18
16.58
16.83
16.66
20.08
20.92
20.34
Non-poor
Poor
Total
24.11
40.48
27.74
15.62
17.27
15.99
20.13
21.49
20.43
Total
20.17
23.70
20.55
Above
university
degree
14.60
18.95
15.06
University
degree
Primarypreparatory
17.16
33.52
18.92
Above
average
degree but
below
university
degree
Can read
and write does not
hold a
degree
Non-poor
Poor
Total
Secondary
degree or
equivalent
Illiterate
Table 8: Educational attainment by poverty status in Egypt, 2008-09
4.16
1.40
3.87
16.08
2.82
14.65
0.56
0.03
0.50
100
100
100
2.32
0.91
1.89
5.64
1.64
4.42
0.09
0.01
0.06
100
100
100
3.21
1.01
2.72
10.69
1.88
8.73
0.32
0.01
0.25
100
100
100
Urban
27.27
19.58
26.44
Rural
24.66
17.43
22.45
All Egypt
25.92
17.87
24.14
Source: ibid.
Table 9: Poverty measurements by educational attainment in Egypt, 2008-09
Illiterate
Can
read
and
write
Primarypreparatory
Secondary
degree or
equivalent
P0
P1
P2
19.04
3.56
1.07
13.51
2.41
0.67
12.39
2.19
0.62
7.96
1.32
0.34
P0
P1
P2
37.72
7.86
2.45
30.82
5.85
1.72
31.38
6.09
1.82
23.68
4.34
1.24
P0
P1
P2
32.35
6.62
2.05
23.94
4.48
1.30
23.32
4.43
1.31
16.41
2.95
0.82
Above
average
degree
University
degree
Above
university
degree
Total
3.90
0.63
0.17
2.07
0.27
0.06
0.56
0.08
0.01
10.75
1.90
0.54
14.64
2.54
0.70
11.29
1.88
0.51
3.21
0.53
0.09
30.50
6.00
1.81
8.21
1.40
0.39
4.77
0.74
0.19
0.96
0.15
0.02
22.17
4.27
1.27
Urban
Rural
All Egypt
Source: ibid.
Education played a more important role in relation to poverty risks in urban areas and for
obtaining an adequate income and thus averting poverty. Poverty was the highest, deepest
and most severe for illiterate individuals and for those with illiterate household heads (Figure
7, tables 8 and 9) in both urban and rural areas. Poverty risks (incidence) for the illiterate
individuals exceeded the average by almost 9% in urban areas and by 7% in rural regions.
Poverty incidence falls continuously, from 32% for the illiterate to 16% for secondary degree
holders and to very low levels for university and post-university graduates (4%). This
suggests the important impact that human capital accumulation has on individual earnings
and on shielding households from poverty.
Figure 7: Poverty incidence (risk) by individual education levels, 2008-09
above
university
university
degree
above
secondary
Rural
secondary
read and
write
illiterate
primarypreparatory
urban
40
30
20
10
0
Source: ibid.
10
The poverty trap
Poverty perpetuated the lack of education, leading to a vicious cycle of poverty and low
education. Such relationships help explain how poverty is transferred from one generation to
the next. A typical scenario can be described as follows: Starting with a household whose
head is illiterate and has no productive assets, the path can be traced through to his
children. The children are very likely to be malnourished – more a consequence of the
parents’ ignorance than the unavailability of adequate food, as well as a result of poor
sanitary conditions. These children are more prone to disease, which further diminishes their
physical capabilities. They also have no place in formal schools. Even if they enter the public
school system, due to the constrained economic conditions of their households, they will
soon drop out to join the labour market. Under these circumstances, many of them will likely
be illiterate and, in the absence of adequate vocational training facilities, these children will
possess limited or very poor skills. The cycle is completed when children marry spouses with
the same characteristics. Thus poverty perpetuates across different generations. Given this
scenario, it is clear that education is a very powerful, though not the only, instrument that can
enable individuals to break the cycle of poverty.
In Egypt, the proportion of individuals with basic education or less who live with household
heads having basic education or less was almost 83% for the poor and 79% for the non-poor
–indicating that even if a non-poor head of household has low education level, household
members would have slightly greater chance of being more educated than were they poor.
This shows that education can greatly inhibit the transmission of poverty.
Employment and poverty links
Changes in employment structure and its productivity can influence both determinants of
change in poverty (growth and distribution components). Growth in employment and its
productivity can improve the growth rate of the economy. Moreover, changes in employment
structure and its productivity can improve income distribution by pushing up the relevant
segment of the Lorenz distribution. This can come about only by increasing employment and
its remuneration.
Most of the poor depends on the only asset they have; labour. Even when a povertyreduction strategy improves the access of the poor to other resources - e.g., land and
capital, physical, financial, infrastructural and human - the process of poverty reduction does
not depend on the creation of an entitlement to rent or annuity for the poor but on the
enhancement of opportunities to be employed more intensively, productively and
remuneratively.
Khan (2007) detected five aspects by which employment can reduce poverty: (a) an
increase in wage employment; (b) an increase in real wage; (c) an increase in selfemployment; (d) an increase in productivity in self-employment; and (e) an increase in the
terms of exchange of the output of self-employment.7 Poverty declines if the aggregate of all
these effects is favourable for the poor. This section identifies links between employment,
unemployment, employment in informal sector, and employment in agriculture on one hand
and poverty on the other hand. Thus, the linkage between employment growth and poverty
can be established.
Participation and unemployment rates8
In Egypt, 59% of individuals aged 15-65 years participated in the labour force in 2008-09,
and the unemployment rate was 4.8%.9 However, large disparities in participation rate and in
unemployment rate can be observed between males and females in urban and rural areas.
11
About four out of five males aged 15-65 years join the labour force, in both urban and rural
areas, with slightly higher participation rates in rural areas (78% in urban areas and 82% in
rural areas). Meanwhile, the participation rate for females was only 27% in urban areas and
45% in rural areas. Unemployment rates differed significantly between males and females
(3.1% for males and 8.5% for females). Similar to other agricultural societies, the
unemployment rate among females was considerably higher in urban areas (6.6% of labour
force was unemployed in rural areas compared to 12% in urban areas). The corresponding
unemployment rates among males were 4.7% in urban areas and 2% in rural areas. Poverty
interacts with gender to produce large differences in unemployment rates between males
and females, thus gender gap is much larger than urban to rural gap or poor to non-poor
gap.
Table 10: Participation and unemployment rates by gender and poverty status, 2008-09
Male
Non-poor
Poor
Total
77.78
78.37
77.84
Non-poor
Poor
Total
82.85
80.78
82.21
Non-poor
Poor
Total
Source: ibid.
80.37
80.29
80.35
Participation rate
Female
Total
Urban
27.17
52.23
24.02
51.86
26.85
52.19
Rural
46.86
64.65
39.45
60.99
44.73
63.56
All Egypt
37.23
58.57
36.21
59.1
37.02
58.68
Male
Unemployment rate
Female
Total
4.54
5.57
4.65
12.13
18.98
12.75
6.53
8.60
6.74
1.90
2.14
1.97
6.47
6.94
6.59
3.58
3.63
3.59
3.15
2.83
3.08
8.49
8.62
8.52
4.87
4.53
4.79
The poor had slightly lower labour participation rates than did the non-poor in both urban and
rural areas (with the exception of males in urban areas). The difference in unemployment
rates between the poor and non-poor were higher in urban areas (2.07% for urban areas
versus a meagre difference of 0.05% for rural areas). In addition, in the urban areas the
unemployment rates for poor females are larger, by a greater extent compared to rural
areas, than for non-poor females. The unemployed are a greater portion of the labour force
for poor females in rural areas than for non-poor females in rural areas. Compared to the
overall urban sector, the rural sector has higher poverty rates, as previously demonstrated,
despite its larger participation rates and lower unemployment rates as the urban region. This
simply lends further support to the idea, which has been widely noted, that in many
developing countries, the poor cannot afford to be unemployed, rather they tend to be underemployed. The inability of household members to participate in income-generating activities
or the seasonal or occasional nature of work, or both, can partly provide an explanation for
poverty in rural areas. This can be explained by the fact that in Egypt, underemployment
rates are higher among the poor, as explained below.
Regular and irregular jobs
Although we cannot offer evidence about invisible underemployment, or low-productivity, we
can offer some evidence regarding the prevalence of visible underemployment among the
poor, compared to the non-poor. Underemployment is defined as working in temporary,
seasonal or casual work or working less than the normal working days per week for lower
wages. As shown in table 11, at the national level, 84% of employed individuals have
permanent work, 4.6% have temporary work, 0.5% have seasonal work, and 10.8% have
casual work. Of the poor 74% have permanent jobs compared to 87% of non-poor
individuals. Casual workers constitute 8.1% of non-poor employed persons and 20.7% of
non-poor. Characteristically, casual workers are more likely to be represented in the poor
12
groups, as the risk of poverty for a person engaged in irregular work is almost double the
rate for the population as a whole and for individuals with permanent jobs. This is reflected
by the poverty incidence among casual workers, which is 41.9% as opposed to 19.4% for
permanent workers.
Table 11: Distribution of working individuals by job regularity and poverty status, 2008-09
Permanent
Non poor
Poor
Total
86.37
73.08
85.01
Non poor
Poor
Total
87.04
74.68
83.49
Non poor
Poor
Total
Source: ibid.
86.76
74.41
84.05
Temporary
Seasonal
Urban
6.21
0.41
6.41
1.07
6.23
0.47
Rural
3.67
0.44
3.58
0.77
3.64
0.54
All Egypt
4.75
0.43
4.07
0.82
4.60
0.51
Occasional
Total
7.01
19.44
8.29
100
100
100
8.85
20.97
12.34
100
100
100
8.07
20.70
10.84
100
100
100
Occasional
Total
24.09
4.58
1.33
10.28
1.73
0.47
48.89
11.15
3.64
28.77
5.39
1.55
41.90
9.30
2.98
21.94
4.04
1.15
Table 12: Poverty measurements by job regularity in Egypt, 2008-09
Permanent
P0
P1
P2
8.83
1.45
0.39
P0
P1
P2
25.74
4.53
1.24
P0
P1
P2
Source: ibid
19.43
3.38
0.92
Temporary
Seasonal
Urban
10.60
23.31
1.62
4.45
0.42
1.45
Rural
28.27
41.26
5.34
7.29
1.58
1.79
All Egypt
19.43
35.13
3.48
6.32
1.00
1.67
Table 13: Rate of under and over employment by poverty status, 2008-09
Rate of under employment
Rural
Total
Males
5.26
7.07
6.14
11.66
14.01
13.51
5.93
9.26
7.77
Females
3.30
7.30
4.57
11.03
21.09
17.36
3.71
9.62
5.76
All Egypt
4.77
7.11
5.82
11.57
14.49
13.83
5.40
9.30
7.41
Urban
Non poor
Poor
Total
Non poor
Poor
Total
Non poor
Poor
Total
Source: ibid
Rate of over employment
Urban
Rural
Total
8.35
13.87
8.93
10.88
11.27
11.00
9.57
11.82
10.07
3.12
5.98
3.27
4.43
4.39
4.42
3.54
4.98
3.67
7.03
12.78
7.57
9.96
10.80
10.21
8.35
11.25
8.93
The relationship between poverty and under-employment is supported by the fact that the
poor represent a higher share of the under-employed group and by the fact that the majority
of the poor are under-employed. This is reflected by the observation that amongst the poor,
13
the share of workers who work less than 5 days per week (denoting under-employment) is
13.8% compared to 5.8% for the non-poor. Moreover, poor females are more likely to be
under-employed, especially in rural areas where 21% of the female working poor in rural
areas are under-employed, as opposed to 7.3% for the female working non-poor. This is
another observation, which lends support to the idea that the poor cannot afford to be
unemployed and are often obliged to settle with whatever employment opportunity is
available (Table 13).
Type of work
As table 14 demonstrates, the majority of employed individuals in 2008-09, of both poor and
non-poor groups, were in the wage employment category (53.5% of the total population).
The incidence of wage employment was higher in urban regions than in rural regions (68.1%
in urban areas versus 44.5% in rural areas-table 14). Conversely, the categories of unpaid
workers are more common in rural areas. This may be due to the fact that rural residents are
engaged primarily in agriculture. In rural areas, unpaid labour represented about one fifth of
the rural population in 2009, and more than a quarter of the rural poor. Unpaid workers and
the unemployed groups are the categories most stricken by poverty. Unpaid workers
represent 21.5% of labour force in rural areas as opposed to only 4.4% in urban areas.
Table 14: Distribution of working individuals by type of work and poverty status, 2008-09
Wage earner
Employers
Non poor
Poor
Total
68.97
60.90
68.13
8.86
6.06
8.57
Non poor
Poor
Total
43.81
46.18
44.49
14.79
11.99
13.99
Non poor
Poor
Total
Source: ibid
54.79
48.85
53.51
12.20
10.91
11.92
Self employed
Urban
11.82
15.13
12.16
Rural
17.50
13.89
16.47
All Egypt
15.02
14.12
14.82
Unpaid worker
Unemployed
Total
3.82
9.32
4.39
6.53
8.59
6.74
100
100
100
20.31
24.31
21.46
3.58
3.63
3.59
100
100
100
13.11
21.59
14.95
4.87
4.53
4.79
100
100
100
Table 15: Poverty measurements by type of work, 2008-09
Wage earner
Employers
P0
P1
P2
9.24
1.57
0.43
7.31
1.06
0.26
P0
P1
P2
29.77
5.95
1.80
24.57
4.21
1.11
19.80
3.82
1.14
19.84
3.35
0.88
P0
P1
P2
Source: ibid
Self employed
Urban
12.87
2.25
0.63
Rural
24.20
4.42
1.26
All Egypt
20.65
3.74
1.06
Unpaid worker
Unemployed
Total
21.95
3.44
0.87
13.19
2.46
0.72
10.34
1.75
0.48
32.49
5.63
1.51
28.99
5.97
1.85
28.68
5.38
1.55
31.31
5.38
1.44
20.51
4.09
1.24
21.69
4.00
1.14
Households that face the highest risk of poverty are affected by a combination of risk factors;
Location interacts with labour market profile to produce different welfare picture among
individuals. Although, the incidence of poverty was the highest among unpaid workers and
agriculture wagers in both urban and rural areas, the risk was higher for those in rural areas,
where, one out of three unpaid workers is poor. In urban areas, the risk to poverty among
the employed is lower than the national average by about 3%.
14
Table 16: The incidence of poverty by type of work, 2008-09
Wage
worker
Urban
Rural
All Egypt
Source: ibid
28.24
49.58
46.95
Agricultural activity
Self
Employer
employed
18.78
26.11
25.42
15.83
24.04
22.93
Unpaid
worker
Wage
worker
27.00
32.96
32.46
8.84
25.44
16.63
Non agricultural activity
Self
Employer
employed
4.22
17.78
10.08
11.73
24.51
17.96
Unpaid
worker
Total
12.36
30.56
23.61
10.28
28.77
21.94
As most of the poor were self-employed in the agricultural sector, or wage workers in private
sector, wage policies enacted by the government and public enterprise sectors may have
little impact on poverty. Policies to reduce poverty must be aimed at legislation regarding
minimum wages paid by private employers, which would still affect only a fraction of the
poor. Thus, devising effective poverty reduction policies necessitates targeting selfemployed workers in agriculture, particularly in rural areas.
Youth employment and poverty
Because labour is the main asset of the poor, making it more productive is the best way to
reduce poverty. Thus to effectively improve the welfare of the poor, enhancing employment
opportunities is key, as well as empowering the poor through developing their human capital,
enabling them to take advantage of these opportunities. This is of crucial importance, for
human development is cumulative, and missed opportunities to invest in and prepare the
youth generation would be extremely costly to reverse, both for young people and for
society.10
Overall employment rate of youth aged 15-29 years stood at 49%, while it is 55% for youth in
age group 18-29 years. Youth employment rate (15-29 years) is slightly higher than the
working age employment rate (15-65 years). As expected, employment rate increases as
age increases. Youth employment rates fall as per capita income increases, as youth devote
more time to schooling. Employment rate is higher for the poor compared to the non-poor for
all ages till the age of 24 when youth graduate from university. Among the youngest workers
(those 15–17), many do not attend school and live in poor households. Thus employment
rate for poor youth is almost double that of the non-poor. Moreover, half of poor youth of
ages 18-20 participated in the labour force, compared to only one third of non-poor youth
(Table 17).
Unemployed youth continued to be of particular concerns. Youth, which make up make up
21% of the Egyptian population, represent 76% of the unemployed. Moreover, the youth
unemployment rate is three times the national unemployment rate, indicating that
unemployment is mainly a youth problem. The World Development Report 2007 stated that:
“Having young people sit idle is costly in forgone output. Estimates indicate that lowering
youth unemployment could raise GDP by anywhere from 0.3 to 2.7% in a range of
Caribbean countries based on forgone earnings alone”. Egypt is no exception.
The unemployment rate for poor youth is lower than that of the non-poor youth, at any age.
As emphasized previously, poor young people cannot afford to stay unemployed, as most
have to work in order to supplement low household income, unfortunately, often at the
expense of their education. This in turn leaves these individuals vulnerable in the long run to
chronic poverty. So the incidence of unemployment may be low, even though the youth are
still in poverty. As indicated by table 17, unemployment rate increases with age, peaks at the
age of 18 and 19 and declines afterwards. This is true for both poor and non-poor youth, yet
the unemployment rate for the poor is always lower than that of the non-poor.
15
Table 17: Labour force participation and unemployment rates among youth, 2008-09
Age
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
All youth
Source: ibid
Poor
16.99
27.26
30.41
44.94
53.65
49.10
49.87
55.15
67.49
66.91
64.86
69.72
68.14
68.87
65.17
52.68
Participation rate
Non poor
9.27
13.20
18.63
33.12
37.42
39.70
41.80
52.56
61.29
65.52
63.13
69.04
68.79
67.57
68.01
48.78
Total
11.53
17.22
22.00
36.31
41.85
42.24
43.81
53.22
62.65
65.83
63.52
69.17
68.66
67.80
67.58
49.71
Poor
poor
2.21
4.22
9.52
13.65
13.60
12.94
13.21
14.66
12.23
9.53
5.00
5.71
3.35
3.65
2.16
Unemployment rate
Non poor
non poor
3.70
3.48
12.79
18.36
19.27
16.21
20.62
19.98
15.74
13.74
7.82
8.70
5.97
3.88
4.04
Total
Total
3.06
3.81
11.50
16.79
17.28
15.18
18.52
18.58
14.91
12.80
7.18
8.12
5.46
3.84
3.76
Unemployment rates continue to be high for secondary and university graduates, especially
for the poor. As presented in table 18, unemployment was more pronounced among the poor
where 29% of poor educated persons of age 18-29 were unemployed and one out of four
educated non-poor people were unemployed. It seems that even if a poor person is able to
break the vicious circle of education and poverty, he/she still cannot compete in the job
market as a result of low quality education, labour market mismatch, or because of a lack of
connections in identifying job opportunities.
Table 18: Youth unemployment rate by education level and poverty status, 2008-09
Poor
Non-poor
All youth
Source: ibid
Secondary
16.05
14.27
14.71
Above secondary
28.67
21.27
22.26
University or higher education
29.37
25.27
25.62
Employment in rural areas
Agriculture and non-agriculture employment linkages
Table 19: Employment status by expenditure quintiles in rural Egypt, 2008-09
nd
st
1 Quintile
Wage worker in agriculture
Self employed in agriculture
Unpaid worker in agriculture
Agricultural Employment Total
Wage worker not in agriculture
Self employed not in agriculture
Unpaid worker not in agriculture
Non-Agricultural Employment Total
Source: ibid.
17.26
21.69
20.75
59.7
31.20
7.55
1.55
40.3
2
Quintile
10.63
23.81
22.93
57.37
33.61
7.69
1.32
42.63
rd
3
Quintile
8.42
24.07
22.70
55.19
36.13
7.74
0.94
44.81
th
4
Quintile
6.03
23.84
20.41
50.28
38.85
9.29
1.58
49.72
th
5
Quintile
3.63
25.84
15.72
45.19
41.23
12.09
1.49
54.81
All rural
8.64
24.00
20.31
52.95
36.62
9.04
1.38
47.04
In 2008/09, more than half of the employed population was engaged in agricultural activities
(53%). A detailed inquiry into the employment status of workers reveals that different
distributions exist across the employment categories: wage workers, self-employed, and
unpaid workers, between workers of the agricultural and non-agricultural sector. For
16
example, 44% of workers in the agricultural sector are either self-employed or unpaid
workers (24% and 20.31%, respectively). This stands in striking contrast with the distribution
of workers across employment categories in the non-agricultural sector. Specifically, in
contrast to only 8.6% of waged workers in the agricultural sector, waged workers in the nonagricultural sector comprise a significantly larger share of 36.62%. Furthermore, an
important shift which must be noted is the transfer of workers in the agricultural sector from
waged employment to self-employment and unpaid employment since 2004-05.
The distribution of the expenditure quintiles across employment categories in the agricultural
and non-agricultural sectors in 2008-09 is not different from the distribution observed in
2004-05. A significant trend, which becomes evident from table 19, is the concentration of
the poorer expenditure quintiles in the agricultural sector and the clustering of the higher
expenditure quintiles in the non-agricultural sector. This is evident from the continually
decreasing share of employment in the agricultural sector across higher quintiles, and also
from the fact that the poorest expenditure quintile comprises 40.3% of employment in nonagricultural sector compared to the richest quintile, which represents 54.81% of employment
in non-agricultural sector. Moreover, 42.44% of the working population in the poorest quintile
is in the agricultural sector, either working as self-employed or as an unpaid worker. This
difference in the distributional patterns of the expenditure quintiles across different
employment categories, though not sufficient alone, suggests that employment in the nonagricultural sector offers more routes out of poverty than the agricultural sector.
Individuals in the two poorest quintiles have a lower likelihood of establishing their own
businesses as independent workers outside agriculture; they either work as agricultural paid
or unpaid workers.
Of the 53% of the working population engaged in the agricultural sector, 79.78% are
comprised of female workers, compared to 39.62% for males (Table 20). This share of
employed females in agriculture sector is only slightly less than the share of 83% in 2004/05.
A similar trend emerged for men. If we further disaggregate figures of 2008-09 into waged
workers, self employed and unpaid workers, we see that 42% of employed women are
unpaid workers in agriculture; 35% are self-employed and only 3% are agricultural wage
earners. The corresponding figures for males are 9.5%, 19% and 11%, respectively. Clearly,
women bear the brunt of vulnerable employment in Egypt. Although agricultural employment
became less important in 2008 compared to 2005, the share of unpaid workers has
increased by 6%. This may be due to higher female participation rate and fewer waged work
opportunities, as women had to work as unpaid workers in agriculture sectors.
Table 20: Employment status by gender in rural Egypt, 2008-09
Wage worker in agriculture
Self employed in agriculture
Unpaid worker in agriculture
Agricultural Employment Total
Wage worker not in agriculture
Self employed not in agriculture
Unpaid worker not in agriculture
Non-Agricultural Employment Total
Source: ibid.
Male
11.41
18.67
9.54
39.62
48.15
10.92
1.30
60.38
Female
3.06
34.72
42.00
79.78
13.42
5.26
1.53
20.21
All rural
8.64
24.00
20.31
52.95
36.63
9.04
1.38
47.05
As previously demonstrated, the distribution of the working population across quintiles
revealed that the richer quintiles clustered out of the agricultural sector into the nonagricultural sector. Table 21, though certainly not enough alone to warrant such an
argument, lends support to these previous findings by demonstrating that with increasing
levels of education, individuals similarly cluster out of the agricultural sector into the nonagricultural sector. Educational attainment seems to be an important determinant of
17
employment in the agricultural and non-agricultural sectors. About 77% of all employed
illiterates are engaged in agricultural activities, 67% are self-employed or unpaid while only
10% are wage workers (Table 21). On the other hand, only 12% of university degree holders
are active in the agriculture sector.
Table 21: Employment status by education status in rural Egypt, 2008-09
Wage worker in agriculture
10.19
Read and
write
8.21
Self employed in agriculture
37.74
27.83
15.31
11.36
4.42
24.00
Unpaid worker in agriculture
29.33
12.37
17.74
15.45
4.94
20.31
Agricultural Employment Total
77.26
48.41
42.83
34.43
11.93
52.95
Wage worker not in agriculture
13.93
37.24
44.88
54.86
79.04
36.63
Self employed not in agriculture
7.72
13.41
10.09
8.93
8.42
9.05
Illiterate
Basic
education
9.78
Secondary
education
7.62
University
education
2.57
All
rural
8.64
Unpaid worker not in agriculture
1.09
0.94
2.20
1.79
0.61
1.38
Non-Agricultural Employment Total
22.74
51.59
57.17
65.57
88.07
47.05
Source: ibid.
Income sources
In 2008-09, Non-agricultural income from either wages or self-employment, in rural
Egypt contributed, on average, about 43% of total income (Table 22). This compared to
about 32% from agricultural wages and self-employment and 25% of other income sources
such as rent and transfers. It is clear that non-agricultural sector is an important source of
income, even at this highly aggregated national level. Examining the contribution of
agricultural/ non-agricultural sources to total income across different per capita
expenditure quintiles indicates that agricultural income is more important than nonagricultural income for the lowest quintile, where the contribution from agricultural sources
is 38% of total income. Taking all non-agricultural incomes sources together, the evidence in
table 22 suggests that the importance of non-agricultural income is also unevenly spread
across quintiles. The share of non agricultural in sources of wage income to total income
increases as per capita income increases, where the share for the highest quintile is higher
by about 2.5% compared to the lowest quintile. On the other hand, non-labour income
provides 21% of the income of the poorest quintile (compared to 31% for the richest quintile).
Table 22: Income shares by sources of income and quintiles, 2008-09
st
1 Quintile
Income from wages
45.03
Income from agricultural wages
13.73
Income from agricultural self employed activities
24.62
Total Agricultural sources of income
38.35
Income from non agricultural wages
31.31
Income from non-agricultural self employed activities9.24
Total Non-Agricultural sources of income
40.55
Income from financial assets
0.05
Income from real estate with imputed rent
0.53
Income from transfers
10.63
Total imputed rent for all household
9.89
Total non-labour sources of income
21.10
Total household income with imputed rent
100
Source: ibid.
nd
2
Quintile
40.17
7.90
28.70
36.6
32.27
10.10
42.37
0.14
0.90
9.71
10.29
21.04
100
rd
3
Quintile
38.52
6.02
29.64
35.66
32.50
10.13
42.63
0.26
1.15
10.05
10.25
21.71
100
th
4
Quintile
36.70
3.88
28.27
32.15
32.82
12.24
45.06
0.32
1.50
10.72
10.25
22.79
100
th
5
Quintile
30.62
1.84
24.52
26.36
28.78
14.19
42.97
0.92
4.28
15.54
9.94
30.68
100
All
rural
36.43
5.25
26.82
32.07
31.07
11.87
42.94
0.46
2.19
12.08
10.11
24.84
100
The importance of non-agricultural income as a route out of poverty becomes more apparent
when we look at the relative importance of agricultural and non-agricultural activities in
labour income. Across quintiles, the share of labour income from non-agricultural sources
(total of wage and self-employed) rises with living standards till the 4th quintile, while the
share of labour income from agricultural sources (total of wage and self-employed)
18
decreases sharply in the richer quintiles (from 38% in the 1st quintile to 26% in the 5 th
quintile). It is also interesting to note that the share of non-labour sources of income rises
from 21% in the 1st quintile to 31% in the 5th quintile. Moreover, the share of non-agricultural
sources of income (total labour and non-labour income) for the richest quintile is higher than
the poorest quintile by 12%.
The above results agree to a great extent with the argument of Lanjouw (1995) who
emphasized that the “non-farm sector is a heterogeneous collection of activities which
includes both productive and non-productive occupations. The former, contributes to growth,
raise living standards. And in general are associated with a dynamic process of inter-sectoral
transfer out of agriculture into manufacturing and services, with specialization, and with
technological changes. The latter are more in the nature of residual activities into which
people are pushed when other sources of income (cultivation income, rents, transfers, etc.)
are not sufficient to make ends meet. For the poor, these activities contribute significantly to
total incomes, but they do not actually generate significant returns. In the labour market, it
appears that the low productivity and high productivity activities can be neatly delineated by
distinguishing between casual and regular employment. Among the own-enterprise activities,
one can less readily distinguish between high and low productivity activities in the absence
of detailed sub-sectoral information. The important implication of these observations is that it
is not obvious how non-farm income shares are likely to evolve in the fact of broad economic
development. While one would expect productive non-farm activities to become relatively
more important with economic progress, the less productive activities would expect to wither
away. As a result, overall non-farm income shares might not rise (although, of course, both
total, and non farm income levels would be expected to rise).”11
Households size, composition, and poverty
Households' composition is important because it is often associated with socioeconomic
differences between households. For example, the size and composition of the household
affects the allocation of financial and other resources among household members, which in
turn influences the overall being of these individuals. Household size is also associated with
crowding in the dwelling, which can lead to unfavourable health conditions.
In Egypt, as in other countries, larger families are more likely to be poorer than smaller ones.
The poor also tend to support a proportionally higher number of children and elderly people
than the non-poor. Table 23 provides basic information on average household size by
poverty status, for urban and rural areas and for the population as a whole in Egypt.
Table 23: Average number of children, adults, elderly and household size by poverty status
and location, 2008-09
Average number
of children
Non-poor
Poor
Total
1.20
1.99
1.26
Non-poor
Poor
Total
1.60
2.37
1.78
Non-poor
Poor
Total
Source: ibid.
1.40
2.29
1.54
Average number
of adults
Urban
2.52
3.85
2.62
Rural
2.65
3.85
2.92
All Egypt
2.59
3.85
2.79
Average number
of elderly
Average
household size
0.34
0.34
0.34
4.07
6.18
4.22
0.33
0.38
0.34
4.58
6.61
5.04
0.33
0.37
0.34
4.32
6.52
4.67
19
It is evident that a poor person typically lives in a bigger household than the non-poor and
the overall average, as poor households have a relatively larger number of children and
elderly people than the national average (Table 23). Differences between the poor and nonpoor, account for 2.11 persons per household in urban areas and 2.03 persons per
household in rural areas. The gap in household size between poor and non-poor households
is wider than the urban/rural gap, suggesting that there is a more significant correlation
between household size and poverty, as the average household size among the poor in
urban and rural areas are somewhat similar (4.22 and 5.04 persons respectively).
Table 24: Poverty measures by household size, 2008-09
One
person
2
persons
3 persons
P0
P1
P2
0.24
0.01
0.00
1.14
0.19
0.06
1.91
0.22
0.05
P0
P1
P2
2.35
0.47
0.16
4.89
0.90
0.25
8.26
1.23
0.31
P0
1.16
P1
0.22
P2
0.07
Source: ibid.
2.84
0.51
0.14
4.99
0.71
0.17
4 persons
5
persons
Urban
3.37
7.22
0.48
1.01
0.10
0.24
Rural
12.47
19.74
1.76
3.06
0.42
0.76
All Egypt
7.84
13.82
1.11
2.09
0.26
0.51
6 or 7
persons
8 persons or
more
Total
17.65
3.08
0.84
39.25
8.40
2.70
10.58
1.87
0.53
36.04
6.75
1.94
54.21
12.38
4.08
29.99
5.89
1.77
29.40
5.42
1.54
51.15
11.57
3.80
22.02
4.24
1.26
Larger households are at a higher risk of poverty. This observation provides at least a partial
explanation of why particular households are poor; the majority of the poor live in
households with 6 or more persons (77%), as the average household figures demonstrated
in table 24, while the non-poor live in smaller households. Overall, poverty rates increase as
the household size increases, as the data presented in table 24 shows. Poverty correlates
strongly with household size. Moreover, for the same incidence of poverty, there are 2-3
more individuals in urban households, implying that for larger families in urban areas the
incidence of poverty is lower than the poverty rates of families of a similar size in urban
areas. In urban areas, practically, no one in a household of only one person is poor. In urban
areas, 23% of individuals living in households of six or more persons are poor, compared to
44% in rural areas. Since households in rural areas are larger, it is not surprising that
poverty is higher in rural areas.
Gender and poverty
Table 25: Poverty measures and contribution to poverty by gender of head of household, 200809
P0
P1
P2
Male
Female
Total
7.58
5.44
7.23
1.26
0.99
1.21
0.34
0.28
0.33
Male
Female
Total
24.33
15.70
22.88
4.51
3.01
4.25
1.30
0.90
1.23
16.70
11.10
15.77
3.03
2.10
2.87
0.86
0.62
0.82
Male
Female
Total
Source: ibid.
Contribution to poverty
Urban
87.65
12.35
100
Rural
88.48
11.52
100
All Egypt
88.31
11.69
100
Population Share
83.59
16.41
100
83.22
16.78
100
83.39
16.61
100
20
As poverty analysis depends on household surveys that collect information on household
expenditure, it is impossible to obtain expenditure of each household member, and hence it
is difficult to distinguish between gender differences in poverty at the individual level. As a
result of such data limitations, the analysis here is carried out at the level of heads of
household. Analysis at the level of male and female-headed households can provide some,
though partial, insights into differences in poverty across genders.
For Egypt, female-headed households 12 (FHH) represent a small proportion of total
households: 16.6% of households were headed by females in 2008-09. Poverty measures
for female-headed households are lower than male-headed households in urban and more
significantly lower in rural areas (Table 25). Differences in poverty rates between urban and
rural areas are greater than differences in poverty rates between FHH and MHH.
Table 26, which shows the share of different income sources by gender, indicates that FHHs
were more vulnerable to economic shocks, as their income sources were often irregular or
insecure. For example, income from transfers was one of the most important sources of
income for FHH, representing 47% of their income, while transfers accounted for only 10.7%
of all income of MHH. Also, wages, which is the first source of income, accounted almost
30.7% of income for FHH and 49% for MHH (Table 26). Agricultural income and income
from enterprises accounted to 37% of all income for MHH, compared to only 17.5% for FHH.
Table 26: Share of different income sources by gender, 2008-09
Wages
Male
49.09
Female
30.68
Total
46.79
Source: ibid.
Income
from
agricultural
activities
Income from non
agricultural
projects
Return from
financial
assets
Income from
real estate
property
Transfers
Total
income
16.92
10.37
16.10
19.89
7.18
18.30
1.24
1.17
1.23
2.21
3.27
2.34
10.65
47.34
15.23
100
100
100
Children in poverty
Information obtained from the 2008-09 survey allow for an assessment of several key
aspects of the welfare of Egypt's children. Questions in the survey, which included current
school attendance and participation in work, were used to estimate the enrolment rate, the
illiteracy rate and child labour among different ages. Finally, this section also considers
information on the routine immunizations received by children, whether they are recorded on
a child's birth record or determined by asking the child’s mother.
Illiteracy among poor children
There was a strong relationship between poverty and the educational attainment of children
in Egypt, but with large gender and sectoral (urban/rural) gaps (Table 27). Overall, illiteracy
rates among children in poor households were higher than that among children in non-poor
households, regardless of their gender, age or place of residence. The notably higher
illiteracy rates of girls in rural areas, 10.32% compared to 3.9% in urban areas may be due
either to cultural behaviours, and/or to the unavailability of schools in their neighbourhoods.
In urban areas, male illiteracy rates were higher than female rates within both poor and nonpoor groups. Yet still, in urban areas, illiteracy rates of poor males were more than three
times the rate of the non-poor.
Poverty interacted with gender to produce larger gaps in educational attainment among
genders in the poor groups in both rural and urban areas. Furthermore, illiteracy rates
among poor children were three times the rate among non-poor children, in both urban and
rural areas. Among poor individuals in rural areas, 13% of males and 18% of females of age
21
12-17 years were illiterate, while the corresponding proportions for non-poor children were
less than half these rates (5 and 6% respectively). Female children in poor households living
in rural areas had the highest probability of being illiterate, compared to urban areas where
illiteracy rates were lower for poor girls than poor boys; indicating again that illiteracy among
girls in rural areas is likely to be more a result of culture rather than economic difficulties.
These children, deprived of even a basic education in childhood, will have very poor labour
market prospects in the future and thus they, and their children, are more likely to be
deemed to live in poverty.
Table 27: Illiteracy rate among children aged 12-17 years, 2008-09
Boys
Urban
3.6
13.4
5.14
Rural
4.76
12.79
7.73
All Egypt
4.25
12.91
6.78
Non-poor
Poor
Total
Non-poor
Poor
Total
Non-poor
Poor
Total
Source: ibid.
Girls
Total
2.86
9.87
3.9
3.23
11.73
4.54
6.18
17.76
10.32
5.45
15.15
8.98
4.73
16.21
7.94
4.48
14.47
7.34
Similar to the observations of studies on other countries, as figure 8 shows, there is a large
difference between the illiteracy rate among children in MHHs and those in FHHs. This
difference is especially pronounced among poor households. More specifically, the illiteracy
rate is 31% among children living in poor FHHs, compared to15% for children in MHHs; the
corresponding figures for non-poor households are 8% and 4%, respectively.
School enrolment
The strong link between poverty and child labour has traditionally been regarded as a well
established fact. Labour interferes with school attendance and hence the learning and
development of a child, leading to a decrease in human capital, which is more likely to
perpetuate poverty. Thus an observation of the levels of school dropout and child labour is
important for identifying the most vulnerable groups to poverty, better enabling policy makers
to take appropriate action. However, it must be mentioned that child labour in and of itself is
not harmful as long as the child stays at school.
Figure 8: Illiteracy rate among children aged 12-17 years by gender of household head, 200809
40
Male headed
Female headed
All households
30
20
10
0
Non poor
Poor
Total
Source: ibid.
22
School enrolment can be thought of as an interaction of two factors: supply and demand. In
other words, low school attendance is in part due to family decisions based on the
opportunity cost of schooling (demand for schooling) and in part on the availability and
quality of school facilities (supply of schooling). Neither side should be neglected when
analysing school attendance patterns. The main causes contributing to child labour are
either educational or economic in nature. Child labour could be a consequence of low quality
and the high cost of education.
The information collected in the 2008-09 survey provides some insight into the
considerations that underlie decisions made at the household level, particularly at different
levels of welfare. At the outset, it must be noted that one of the most important questions
concerning the nature of poverty in any county is whether the poor constitute the same
group of people over long periods of time, or whether there are large numbers that enter and
exit the ranks of the poor over the years. An equally important aspect of this issue is whether
children who come from poor families are likely to be poor when they become adults and
have their own families. Given the strong positive correlation between education and levels
of welfare proven in the previous sub-sections, the relationship between welfare levels and
school attendance of children is also given special attention in table 28.
Table 28: School enrolment for children aged 6-15 years, 2008-09
Boys
Girls
Total
95.76
86.1
94.49
95.69
85.57
94.32
93.39
81.9
89.52
93.62
84.61
90.56
94.45
82.72
91.42
94.54
84.8
91.98
Urban
Non-poor
Poor
Total
95.62
85.08
94.17
Rural
93.84
87.12
91.54
All Egypt
94.62
86.72
92.52
Non-poor
Poor
Total
Non-poor
Poor
Total
Source: ibid.
Overall net enrolment rate in basic schools reached 94.3% in urban areas and 90.6% in rural
areas (Table 28). Poverty correlates strongly with school participation of children, given the
lower enrolment rates for poor children in both rural and urban areas. The corresponding
figures for secondary school enrolment are 77.4% and 65.8% for urban and rural areas,
respectively as shown in table 29.
Table 29: School enrolment for children aged 16-17 years, 2008-09
Boys
Girls
Total
81.23
59.81
77.92
81.41
56.04
77.38
69.26
49.75
62.36
72.11
54.97
65.77
74.34
51.74
67.98
76.16
55.18
70.02
Urban
Non-poor
Poor
Total
81.59
52.74
76.88
Non-poor
Poor
Total
75
59.57
69.05
Non-poor
Poor
Total
Source: ibid.
77.93
58.21
71.95
Rural
All Egypt
23
The difference between poor and non-poor households in the proportion of children enrolled
in basic schools is about 10%. There is also a large gender gap in school enrolment. As
expected, the same trends observed previously for gender disparities in illiteracy rates hold
for enrolment rates. Similarly, larger differences exist between poor girls and boys compared
to non-poor girls and boys, where 19% of poor girls in rural areas are not enrolled as
opposed to 13% for poor boys (for the non-poor, the corresponding figures are 7% and 6%,
respectively). This furthers the suggestion that in rural areas, gender discrepancies are more
a result of culture than of poverty, as enrolment rates for poor boys, though slightly, are less
than enrolment rates for poor girls in urban areas. It is also worthy to note that children living
in female headed households are slightly more disadvantaged.
The gender, rural-urban, and poor versus non-poor discrepancies in enrolment rates in basic
schools also holds for enrolment rates in higher levels of education. In higher levels of
education, most of the adolescents, who leave school to seek employment before or just
after completing basic education, are from poor families. The difference between the
enrolments of poor and non-poor is more pronounced in secondary education. The urban
region has the highest attendance rates in general and among the non-poor, (with 56%
enrolment rates for the poor compared to 81% for the non-poor). Rural areas, once again,
have a larger gender gap, for both poor and non-poor groups, though this gap is more
pronounced for the poor group. Coupled with the premise that education is positively
correlated with household welfare, it appears that the rural area in general is in need of
targeted efforts to enhance education opportunities, especially for girls.
Child labour
Many empirical studies on child labour have discerned a strong relationship between child
labour and poverty. Poverty is thus viewed as the main determinant of child labour, and, as
argued by the ILO, child labour, in turn perpetuates poverty, interfering with the human
capital development of children by either forcing children to drop out of schools or making
the learning process in schools ineffective. This has led some studies to examine the
relationship between the child labour phenomenon and the participation rate of children in
schools.
Table 30: Percentage of working children by gender and poverty status, 2008-09
Male
Female
Total
1.96
4.38
2.32
6.04
16
7.57
3.04
5.18
3.81
9.15
15.02
11.28
2.57
5.02
3.26
7.79
15.21
9.92
Urban
Non-poor
Poor
Total
9.96
26.43
12.55
Non-poor
Poor
Total
14.96
23.86
18.26
Non-poor
Poor
Total
Source: ibid.
12.78
24.37
16.17
Rural
All Egypt
In general, the findings of these studies confirm that child labour has a negative impact on
the level of school participation. In developing economies, the child may often be a net
contributor to the household's income, while in the industrialized economies he or she is not.
The incidence of child labour may be high in industrialized economies but the children either
merely perform small tasks in the house to assist their parents or work in order to finance
their own (above subsistence level) consumption. Thus, child labour need not necessarily be
24
“bad”, or warrant action from policy makers. Indeed, some (low, non-human capital inflicting)
levels of child labour may even stimulate the children in their personal development as well
as generate a natural attachment to the labour market at an early age. Thus, child labour
can be beneficial, rather than harmful, as long as it is not undertaken at the expense of
educational attainment. Child labour can assist poor families fulfil their needs without
sacrificing the children’s future. In fact, some children may not be able to go to school
without working.
Egypt follows the model of developing countries. Household’s poverty level was strongly
correlated with the proportion of working male children in the household, especially in urban
areas. The gap between poor and non-poor, in this respect, is 10%, in urban areas, (16% for
the poor and 6% for non-poor). Moreover, about one quarter of poor boys aged 12-17 years
had to work in both urban and rural areas, while the percentage of non-poor working boys
was 15% in rural areas and 10% in urban areas. The percentage of working girls was small,
and slightly more prevalent in rural areas compared to urban areas.
Merging this observation with the fact that the illiteracy rate among children aged 12-17 was
higher for girls than boys, it can be inferred that girls who do not go to school in poor
households are kept at home to do domestic work, while boys go to work to help their poor
families.
Housing conditions, access to public water and public facilities
Housing conditions and access to public amenities are an important measure of welfare,
both directly through increased utility and indirectly through their impact on health. The
health status of individuals is positively related to access to potable water, sewerage system,
housing conditions, and the availability of healthy fuel. Since the survey did not collect
information directly pertinent to the health status of individuals in the sample, access to basic
services of water and sewerage system, housing conditions and the availability of healthy
fuel in the sample will be used as proxy indicators for the health conditions of the poor.
As the survey shows, the poor had worse overall housing and living conditions than the nonpoor. Table 31 gives the distribution of access to potable water and other housing
characteristics by poverty status. Access to clean water is achieved either through a
connection to public service, well water or purchased water.
There were marked differences in access to sanitation facilities between rural and urban
areas, where only 88% of the rural compared to 94% of the urban population was connected
to a sewerage network either public or private network. The gap in access between the poor
and for the non-poor is also clear from the data. In rural areas, 90% of the non-poor had
access, while only 84% of the poor did. Similar results were observed in urban areas with a
larger gap between the poor and non-poor. Connection to public water and electricity
networks is almost universal, with slightly lower access for poor households.
Clear differences were observed between poor and non-poor households in both urban and
rural areas regarding wall material, where 16% of the poor lived in houses with unimproved
wall materials as opposed to only 5% of non-poor households.
Indoor pollution from solid fuels (cordwood, kerosene and coal) is a major killer, particularly
of children under age five, thus the usage of healthy fuel (gas and electricity) in cooking is an
appropriate proxy for measuring the health of individuals. Almost all of the urban non-poor
and 98% of the poor used healthy fuel in cooking, while in rural areas, 99% of the non-poor
and 96% of the poor used healthy fuel in cooking.
25
Data from the survey also presents information on waste disposal practices, from which a
large urban/rural gap becomes immediately evident, revealing that 83% of households in
urban areas threw their wastes in the allocated place or they were collected from home
compared to only 26% among households in rural areas. However, poverty status also
affects garbage collection methods significantly, in urban areas, 60% of poor households
use an allocated place, compared to 84% among non-poor urban households, and the
corresponding figures for rural areas are 15% for the poor and 30% for the non-poor.
Table 31: Percentage of households with housing facilities by poverty status and location,
2008-09
improved
water
connection
connected to
electricity
network
Non-poor
Poor
Total
99.73
99.04
99.66
99.87
98.76
99.76
Non-poor
Poor
Total
98.95
98.07
98.69
99.47
98.4
99.15
Non-poor
Poor
Total
Source: ibid.
99.32
98.26
99.09
99.66
98.47
99.4
Use improved
fuel for
cooking
Urban
99.72
98.3
99.57
Rural
99.14
96.3
98.29
All Egypt
99.41
96.7
98.81
improved
wall
materials
improved
sewerage
disposals
improved garbage
collection facilities
98.63
90.68
97.79
95.37
86.09
94.39
84.22
59.78
82.46
91.19
82.53
88.59
90.24
83.95
88.35
29.56
14.52
26.12
94.69
84.14
92.37
92.66
84.38
90.83
56.91
23.93
51.71
Multivariate Poverty Profile and Simulations
So far, the report has documented the incidence and changes in poverty rates from 2005 to
2008. These indicators are intrinsically ex-post measures of well-being. At the same time
poverty-reducing policies are forward looking. Policy makers try to design interventions that
protect populations from future poverty. Such interventions are often based on an ex-ante
assessment of probability to fall into poverty. To assess the probability of households in
Egypt to be poor the report relied on a two-step approach. Let total household consumption
Ci be a function of household characteristics Xi and assume that Ci is log-normally
distributed. In the log form:
ln(Ci)= Xi β + εi
(1)
where εi is a normally distributed error term. Then the probability of household i to be poor is:
Pi=prob(ln(Ci)<ln(zi))= Φ ((ln(zi)- Xi β)/ σ)
(2)
where zi is the household-specific poverty line, σ is the standard deviation of the regression,
and Φ is a standard normal distribution function. Thus, in the first stage, a model of the
determinants of household consumption in the form of equation (1), is estimated. In the
second stage, we simulate the effect of the covariates from the consumption regression on
the probability that a household will be poor.
The poverty profile presented in the previous section provides guidelines for the selection of
the potential variables to be included in this regression. As a dependent variable in the
consumption regression we used the log of the total per capita household consumption. The
set of explanatory variables includes household size, household demographic variables such
as share of children, adults, individual working status and his economic activity,
26
characteristics of the household head that include gender, age and age squared, and a set
of dummies for the head’s educational level, and his working status and regional dummies.
Separate regressions were estimated for urban and rural areas in Egypt. Similar to Datt and
Jolliffe (1998), a fixed effect regression specification on the governorate level was used to
correct the bias in the estimated coefficients due to potential endogeneity or omitted variable
bias. Local characteristics, such as the degree of infrastructure development, geographical
location, fertility of land, etc., while not registered in the study data, might affect the level of
consumption of the households living in particular locality. Omitting these variables in the
study’s specification could lead to inconsistency of parameter estimates. The fixed effect
specification allowed for control of this type of omitted variable bias.
Consumption regression results
Annex table 13 shows the results of the consumption regression for urban and rural areas.
Focusing first on household demographics, household size has a significant and negative
effect on the level of household per capita consumption in both urban and rural areas. The
elasticity of total household consumption to household size varies from -0.56 in urban areas
to -0.61 in rural areas (in 2009). These elasticities are comparable with the elasticities
reported in earlier studies. For example, Datt et al. (2001) reported an elasticity of about 0.55 for Egypt in 1997.
Household demographic composition has a strong and significant effect on the level of
household per capita consumption. Controlling for household size, the presence of children
aged 0 to 6 has the strongest negative effect on household consumption. Larger shares of
children aged 7 - 16, the elderly and adult females also generally decrease household
consumption.
Consistent with the descriptive results in the previous section, characteristics of the
household head are important determinants of household consumption. The positive and
significant coefficient on the household age variable indicates that households with older
heads attain higher levels of consumption.
Households headed by females are slightly better off in rural areas (with no statistical
differences in urban areas).13 The educational level of the head has a strong impact on the
household’s level of well-being. During the last decade, Egypt experienced a skill-shortage in
fields such as engineering and computer science. The lack of capacity pushed up the wages
of workers with special types of university education and increased the wage gap across
skill-levels. Technical secondary education, often geared towards traditional industrial
occupations, is no longer in demand. Similarly, low-skill jobs of the type held by workers with
primary education or less, have shrunk in number.
Relative to the omitted category-households in which heads are illiterate-households with the
educated heads have a significantly higher per capita consumption. Moreover, the return on
a head’s education is the highest for urban households, where households with universityeducated heads have about 64% higher per capita consumption than households with
illiterate heads. For households residing in rural areas of Egypt this difference is only about
34%.
It was also found that households in urban areas that have heads employed working in
manufacturing, trade or services have a significantly higher per capita consumption level
(notably higher for those working in manufacturing) than households whose heads work in
agriculture, or are unemployed.
27
Simulations
The estimates of the consumption regression make it possible to simulate the impact of
various parameters on the probability that a household will be poor. Although the data allows
for the simulation of various scenarios, we chose those that, from our perspective, are most
relevant for policies aimed directly at reducing poverty.14
The authors find that a new-born child increases risks of poverty in all regions of Egypt. The
effect of childbirth on the probability of being poor is larger in urban areas (Annex table 12).
In 2009, families with no children and with a newly born child were 21% more likely to be
poor in urban areas and 8% in rural areas. If a household is headed by female rather than
male, the change in poverty is insignificant except for urban areas in 2009, where poverty is
dropped by 4%. If the education status of the head, changes from "illiterate" to "can read and
write -does not hold a degree" the probability of being poor would be dropped by more than
20% in both urban and rural areas, and changes are highly significant in urban areas.
Moreover, changes in the employment status of the household head from "permanent" to
"temporary", would increase poverty by 17% in urban areas and by 14% in rural areas (in
2009). The result of a change in employment status from "permanent" to "occasional" would
increase poverty rate by 35% in urban areas and 24% in rural areas (in 2009).
Large households are more likely to be poor compared to small households where
increasing the household by one person increases the probability of poverty by 1.7% in
urban areas and 2.2% in rural areas (in 2009).
The likelihood of poverty in lower and upper urban areas is higher than Metropolitan region
and higher in Upper region compared to lower region in both urban and rural areas. In 2009,
FHHs were 8% less likely to be poor than MHHs, in urban areas and 22% less likely in rural
areas.
Households with the head working permanently are less likely to be poor compared to
households under another employment status. Relative to households with the heads who
are working permanently, households with the heads in temporary jobs have higher risks of
poverty; seasonal or occasional employment increases the risk of poverty. In urban areas,
for example, households with heads employed as occasional workers are 38% more likely to
be poor than those with heads in permanent jobs. The poverty risks are the highest for
households with heads employed in agriculture. In 2009, the probability of being poor for
households whose heads work in manufacturing was much lower (lower than those working
in agriculture (36% lower in urban areas and 31% lower in rural areas).
To estimate the impact of education on the probability of being poor, we vary the head’s
level of education. All other variables are kept at the sample mean levels. Consistent with
the descriptive results of the previous section, the household head’s educational level is a
strong determinant of the household poverty status. With increasing levels of education, the
education of the household head had greater impact on household poverty in urban areas.
We observed a steady decrease in the risk of poverty for households headed by
respondents with higher levels of education. In 2009, relative to households with illiterate
heads, the probability of being poor was about 18% lower for households with heads that
could read and write, about 45% lower for households with heads possessing basic
education, and 72% lower for the households headed by high school graduates. In all
regions of Egypt, households in which the heads hold postgraduate degrees were almost
twice less likely to be poor than households with illiterate heads.
28
Endnotes
1
World Bank 2002
Living standards in Egypt are monitored with Household Income, Expenditures and Consumption Surveys
(HIECS), conducted in their current format by the Central Agency for Public Mobilization and Statistics
(CAPMAS) since 1990. These surveys have been the main (and the only official) source for poverty and
inequality data in Egypt. The most recent survey was conducted during 2008-2009.
3
a detailed description of the methodology can be found in Annex 1 of World Bank 2002
4
IMF 2009, Ministry of State for Economic Development and the World Bank 2007, World Bank 2009 and UNDP
2009
5
Datt and Ravallion, 1992
6
Geographically, Egypt is divided into seven regions: Metropolitan; including Cairo, Alexandria, Port Said and
Suez governorates , Lower Urban and Lower Rural; which include urban and rural areas of Damietta, Dakahlia,
Sharkia, Qualiobia, Kafr el Sheikh, Garbeyya, Menoufia, Beheira, Ismailia governorates, Upper Urban and Upper
Rural ; which include urban and rural areas of Giza, Bani Suef, Fayoum, Menia, ,Assiut, Sohag, Qena, Aswan
and luxor governorates, and Border Urban and Border Rural ; which include urban and rural areas of Red Sea,
New Valley, Matrouh, North Sinai and South Sinai governorates.
7
The analytical framework in this part of the paper is more fully discussed in Khan (2001), the first discussion
paper in the Issues in Employment and Poverty Series.
8
This section is based on results detailed in Annex Tables A.3.6a through A.3.14b for 2008.09.
9
Using a broad definition of employment to include housewives who work and students who work.
10
World Bank 2007
11
Lanjouw 1995: 11
12
Female-headed households are defined as those households who identified their head as female members as
well as those households who declared that they are headed by male members but they are away from home by
more than six months.
13
This is a result that is frequently appear in income poverty as we found in most cases that female headed
households are widows with small household size and thus they are less poor. However, female headed
households with children are poorer. Their children are more likely to drop out of school and work and hence they
are not income poor.
14
World Bank 2004
2
29
REFERENCES
Datt, G. and D. Jolliffe. 1998. "Poverty in Egypt: Modeling and Policy Simulations."
Gaurav, D. Jolliffe and M. Sharma. 2001. "A Profile of Poverty in Egypt" African Development
Review/Revue Africaine de Developpement Vol. 13(2): 202-37.
El Laithy, H., M. Lokshin and A. Banerji. 2010. “Poverty and economic growth in Egypt, 1995 2000" Journal of African Studies and Development Vol. 2(5).
Khan, R. 2007. “Growth, Employment and Poverty: An analysis of the vital nexus based on some
recent UNDP and ILO/SIDA studies”. DESA working Paper no. 49, Economic and Social Affairs.
Lanjouw, J. and P. Lanjouw. 1995. “Rural Nonfarm Employment: A survey”. Background paper for
World Development Report 1995. The World Bank. Mimeo, IFPRI, Washington, D.C.
Marotta D., R. Yemtsov, H. El-Laithy, H. Abou-Ali, and S. Al-Shawarby, 2009. “Was growth in
Egypt between 2005 and 2008 pro-poor? From static to dynamic poverty profile”, Policy Research
Working paper, No 5589, World Bank
Ravallion, M. 1992. “Poverty Comparisons: A Guide to Concepts and Methods,” LSMS Working
Paper No. 88, The World Bank.
World Bank. 2007. “World Development Report 2007: Development and the next generation”. The
World Bank, Washington D.C.
World Bank. 2002. “Poverty reduction in Egypt - diagnosis and strategy” Report No. 24234-EGT.
World Bank. 2004. “A Poverty Reduction Strategy for Egypt”, Report No. 27954-EGT.
30
ANNEX TABLES
Table 1: Poverty measures by governorates, 2008/09
Governorate
Urban
P0
Rural
Total
Cairo
Alexandria
Port Said
Suez
7.62
6.41
4.43
1.94
7.62
6.41
4.43
1.94
Damietta
Dakahlia
Sharkia
Qualiobia
Kafr el Sheikh
Garbeyya
Menoufia
Beheira
Ismailia
1.20
4.82
10.76
3.43
5.56
5.24
9.39
17.64
12.14
1.05
10.99
21.61
16.83
12.61
8.60
19.90
24.87
23.90
1.11
9.30
19.15
11.33
11.20
7.64
17.93
23.51
18.84
Giza
Bani Suef
Fayoum
Menia
Assiut
Sohag
Qena
Aswan
luxor
11.84
33.44
15.85
16.13
38.65
32.99
34.82
28.72
8.39
37.69
44.03
32.12
33.73
68.21
51.34
40.12
49.60
28.33
22.97
41.45
28.71
30.93
60.97
47.54
39.02
40.92
18.44
Red Sea
New Valley
Matrouh
North Sinai
South Sinai
All Egypt
1.46
0.00
1.17
15.61
0.00
10.98
43.57
11.64
10.95
44.91
0.00
28.94
4.00
6.24
4.32
27.87
0.00
21.56
P1
Urban
Rural
Metropolitan
1.27
1.02
0.93
0.28
Lower Egypt
0.23
0.11
0.66
1.28
1.16
2.84
0.46
2.52
0.63
1.99
0.54
1.02
1.26
2.91
2.60
3.56
1.76
4.06
Upper Egypt
2.17
8.26
5.66
8.07
2.20
5.48
2.89
5.97
9.09
19.16
7.91
11.53
7.80
8.08
6.21
11.03
1.22
5.31
Border regions
0.48
8.82
0.00
1.34
0.07
1.91
2.81
9.39
0.00
0.00
1.97
5.58
P2
Rural
Total
Urban
Total
1.27
1.02
0.93
0.28
0.34
0.27
0.31
0.08
0.16
1.11
2.46
1.68
1.72
0.88
2.60
3.38
3.07
0.06
0.13
0.18
0.11
0.08
0.09
0.27
0.66
0.39
0.02
0.25
0.59
0.59
0.48
0.20
0.66
0.80
1.06
0.03
0.22
0.50
0.40
0.40
0.17
0.59
0.77
0.77
4.79
7.48
4.79
5.48
16.70
10.78
8.03
9.03
3.28
0.61
1.42
0.50
0.71
3.08
2.62
2.48
2.04
0.25
2.63
2.22
1.36
1.58
7.14
3.73
2.53
3.44
1.74
1.48
2.03
1.18
1.44
6.15
3.50
2.52
2.85
1.00
0.98
0.72
0.66
5.56
0.00
4.10
0.17
0.00
0.00
0.68
0.00
0.56
2.18
0.21
0.36
2.59
0.00
1.66
0.29
0.11
0.12
1.48
0.00
1.20
0.34
0.27
0.31
0.08
Source: Author’s calculations using HIECS 2008-09.
Table 2: Poverty by household head's status of employment, 2005 and 2009
Wage earner
Self employed hiring others
Self employed working alone
Unpaid worker
Unemployed
Out of labour force
Out of human force
Total
Poverty headcount rate
2005
2009
Change
18.8
20.7
2.0
22.4
25.9
3.6
21.4
21.9
0.5
29.5
32.6
3.1
21.6
21.4
-0.2
16.0
19.1
3.2
15.7
21.7
5.9
19.6
22.0
2.5
Distribution of the poor
2005
2009
Change
48.4
45.6
-2.8
28.0
27.8
-0.3
12.2
11.4
-0.8
0.2
0.2
0.1
0.3
0.3
-0.0
6.8
8.3
1.5
4.1
6.5
2.3
100.0
100.0
0.0
Distribution of population
2005
2009
Change
50.5
48.4
-2.1
24.5
23.6
-0.9
11.2
11.5
0.3
0.1
0.2
0.1
0.2
0.3
0.0
8.3
9.6
1.2
5.1
6.6
1.4
100.0
100.0
0.0
Source: Author’s calculations using HIECS 2008-09 and Hiecs 2004-05
Table 3: Poverty by education level, 2005 and 2009
Illiterate
Can read and write -does not
hold a degree
Below average degree primary-preparatory
Average degree -secondary
degree -equivalent
Above average degree but
below university degree
University degree
Above university degree
Total
Poverty headcount rate
2005
2009
Change
28.5
32.3
3.9
Distribution of the poor
2005
2009
Change
41.2
40.5
-0.7
Distribution of population
2005
2009
Change
28.6
27.7
-0.8
20.8
23.9
3.2
17.5
17.3
-0.2
16.6
16.0
-0.6
20.9
23.3
2.4
21.4
21.5
0.1
20.2
20.4
0.2
14.3
16.4
2.1
16.7
17.9
1.2
23.1
24.1
1.0
8.6
8.2
-0.4
1.2
1.0
-0.2
2.7
2.7
0.0
4.7
0.2
19.6
4.8
1.0
22.0
0.0
0.7
2.5
2.1
0.0
100.0
1.9
0.0
100.0
-0.2
0.0
0.0
8.6
0.3
100.0
8.7
0.2
100.0
0.2
-0.0
0.0
Source: ibid
31
Table 4: Poverty by household head's education level, 2005 and 2009
Illiterate
Can read and write -does not
hold a degree
Below average degree primary-preparatory
Average degree -secondary
degree -equivalent
Above average degree but
below university degree
University degree
Above university degree
Total
Poverty headcount rate
2005
2009
Change
31.3
35.5
4.1
Distribution of the poor
2005
2009
Change
57.7
58.5
0.8
Distribution of population
2005
2009
Change
36.0
36.3
0.3
21.6
24.0
2.5
21.6
17.8
-3.8
19.6
16.3
-3.3
14.2
17.3
3.0
6.1
8.5
2.4
8.4
10.8
2.4
10.8
12.6
1.8
11.2
12.1
0.9
20.3
21.2
0.9
7.6
7.9
0.3
1.4
1.4
-0.0
3.6
3.8
0.2
3.4
0.0
19.6
3.3
1.2
22.0
-0.1
1.2
2.5
2.0
0.0
100.0
1.7
0.0
100.0
-0.4
0.0
0.0
11.6
0.5
100.0
11.0
0.5
100.0
-0.6
-0.0
0.0
Source: ibid
Table 5: Poverty by household head's gender, 2005 and 2009
Male
Female
Total
Poverty headcount rate
2005
2009
Change
20.1
22.4
2.4
15.3
18.9
3.5
19.6
22.0
2.5
Distribution of the poor
2005
2009
Change
91.7
89.8
-1.9
8.3
10.2
1.9
100.0
100.0
0.0
Distribution of population
2005
2009
Change
89.5
88.1
-1.4
10.5
11.9
1.4
100.0
100.0
0.0
Source: ibid
Table 6: Poverty by demographic composition, 2005 and 2009
no children
1
2
3 or more children
Poverty headcount rate
2005
2009
Change
18.0
19.2
1.2
19.3
21.7
2.4
20.6
22.4
1.8
30.2
38.1
7.8
1
2
3
4
5
6
7 or more
Total
0.6
3.0
5.2
7.6
12.8
23.3
44.5
19.6
1.2
2.8
5.0
7.8
13.8
24.5
45.6
22.0
0.5
-0.1
-0.2
0.3
1.0
1.2
1.1
2.5
Distribution of the poor
2005
2009
Change
46.1
40.3
-5.7
25.6
25.9
0.3
18.9
19.8
0.8
9.4
14.0
4.6
Household size
0.0
0.1
0.0
0.8
0.6
-0.2
2.7
2.0
-0.7
7.3
6.5
-0.8
15.2
14.2
-1.0
21.3
18.9
-2.4
52.7
57.8
5.1
100.0
100.0
0.0
Distribution of population
2005
2009
Change
50.1
46.2
-3.8
25.9
26.2
0.4
18.0
19.5
1.5
6.1
8.1
2.0
1.5
5.2
10.1
18.9
23.3
17.9
23.2
100.0
1.1
4.3
8.8
18.3
22.7
17.0
27.9
100.0
-0.4
-1.0
-1.3
-0.6
-0.6
-0.9
4.8
0.0
Source: ibid
Table 7: Poverty by sector of employment, 2005 and 2009
Government
Public
Private
Outside establishment
Others
Total
Poverty headcount rate
2005
2009
Change
10.4
11.3
0.9
9.0
8.8
-0.2
14.4
15.4
1.0
26.9
31.3
4.4
5.9
20.3
14.4
19.6
22.0
2.5
Distribution of the poor
2005
2009
Change
11.4
9.9
-1.5
1.7
1.2
-0.5
22.4
21.4
-1.1
64.4
67.0
2.7
0.1
0.5
0.4
100.0
100.0
0.0
Distribution of population
2005
2009
Change
20.9
19.2
-1.8
3.6
3.1
-0.6
29.7
30.4
0.7
45.6
46.9
1.3
0.2
0.5
0.3
100.0
100.0
0.0
Source: ibid
Table 8: Poverty by sector of employment of household head, 2005 and 2009
Government
Public
Private
Outside establishment
Others
Total
Poverty headcount rate
2005
2009
Change
16.2
17.1
0.9
13.1
11.0
-2.1
13.7
14.3
0.6
29.9
33.0
3.1
8.8
22.0
13.2
19.6
22.0
2.5
Distribution of the poor
2005
2009
Change
21.5
19.1
-2.4
3.9
2.3
-1.6
20.9
18.9
-1.9
53.6
59.2
5.5
0.1
0.5
0.4
100.0
100.0
0.0
Distribution of population
2005
2009
Change
26.8
25.0
-1.7
6.0
4.6
-1.4
30.8
29.7
-1.1
36.2
40.1
3.9
0.3
0.5
0.3
100.0
100.0
0.0
Source: ibid
32
Table 9: Poverty by economic activity, 2005 and 2009
Economic activity
Agriculture
Mining
Manufacturing
Electricity
Construction
Trade, hotel and restrant
Transport
Finance and real estate
Public and family services
Not applicable
Total
Poverty headcount rate
2005
2009
Change
26.8
31.1
4.4
9.2
12.7
3.6
14.6
17.1
2.4
9.2
10.1
1.0
24.7
29.4
4.7
15.4
16.8
1.3
11.7
17.0
5.3
4.9
6.2
1.3
10.9
11.8
0.8
19.9
22.1
2.2
19.6
22.0
2.5
Distribution of the poor
2005
2009
Change
20.0
19.1
-0.9
0.0
0.1
0.1
3.0
3.0
0.0
0.1
0.2
0.0
3.3
4.3
0.9
3.7
3.5
-0.2
1.4
1.5
0.1
0.1
0.2
0.0
4.4
4.3
-0.1
63.9
63.9
0.0
100.0
100.0
0.0
Distribution of population
2005
2009
change
14.6
13.5
-1.1
0.1
0.1
0.1
4.0
3.8
-0.1
0.3
0.4
0.1
2.6
3.2
0.6
4.7
4.6
-0.1
2.3
1.9
-0.4
0.6
0.6
-0.0
7.9
8.0
0.2
62.9
63.7
0.8
100.0
100.0
0.0
Source: ibid
Table 10: Poverty by the status of employment, 2005 and 2009
Employment status
Severely under employed
Under employed
Employed_normal working days
Over employed
Unemployed
Out of labour force
Out of human force
Total
Poverty headcount rate
2005
2009
Change
34.0
30.4
-3.6
33.7
35.8
2.1
15.9
18.4
2.6
22.2
25.7
3.5
25.2
20.8
-4.4
20.8
23.0
2.2
16.0
19.8
3.8
19.4
22.0
2.6
Distribution of the poor
2005
2009
Change
0.8
0.9
0.0
1.7
2.3
0.5
17.2
17.7
0.5
16.4
15.2
-1.2
2.8
1.7
-1.1
46.9
46.5
-0.4
14.2
15.8
1.6
100.0
100.0
0.0
Distribution of population
2005
2009
Change
0.5
0.6
0.2
1.0
1.4
0.4
21.1
21.1
0.0
14.3
13.0
-1.4
2.1
1.8
-0.4
43.8
44.5
0.7
17.2
17.6
0.4
100.0
100.0
0.0
Source: ibid
Table 11: Poverty by Household Head's Status of Employment, 2005 and 2009
Employment status of the
household head
Severely under employed
Under employed
Employed normal working days
Over employed
Unemployed
Out of labour force
Out of human force
Total
Poverty headcount rate
2005
2009
Change
25.6
28.3
2.7
34.0
33.9
-0.1
17.3
19.5
2.2
24.6
27.2
2.5
15.8
21.4
5.6
15.9
19.1
3.2
16.0
21.7
5.7
19.4
22.0
2.6
Distribution of the poor
2005
2009
Change
1.1
1.4
0.3
3.5
3.9
0.4
50.5
48.5
-2.0
33.3
31.2
-2.1
0.2
0.3
0.1
7.1
8.3
1.2
4.3
6.5
2.2
100.0
100.0
0.0
Distribution of population
2005
2009
Change
0.9
1.1
0.3
2.0
2.5
0.5
56.7
54.7
-2.1
26.3
25.3
-1.0
0.2
0.3
0.0
8.6
9.6
0.9
5.2
6.6
1.3
100.0
100.0
0.0
Source: ibid
Table 12: Changes in the probability of being in poverty (%), 2005 and 2009
2005
Demographic event, child born in the family:
Change from having no children 0-6 years old to having 1 child
Change from having no children 0-6 years old to having 2 children
Change of household head (i.e., followed from a divorce, migration, etc.):
change from "Male" to "Female"
Education event, change in household's head education:
change from "illiterate" to "can read and write -does not hold a degree"
change from "illiterate" to "below average degree -primary-preparatory"
change from "illiterate" to "average degree -secondary degree -equivalent"
change from "illiterate" to "above average degree but below university degree"
change from "illiterate" to "university degree"
change from "illiterate" to "above university degree"
Sector of employment event, household head's sector of employment:
change from "permanent" to "temporary"
change from "permanent" to "seasonal"
change from "permanent" to "occasional"
2009
Urban
Rural
Urban
Rural
28
60.800***
12
26.90
21
44.500***
8
18.10
5.700
-14.90
-4.100***
-17.20
-29.400**
-47.400***
-60
-71.500
-92.600***
-99.400
-21.70
-34.40
-43
-59.50
-71.90
-97.50
-21.700***
-41.000***
-62
-75.400
-92.500
-99.600***
-19.90
-29.50
-42
-61.30
-72.00
-94.30
10.400
-15.300
39.000
8.50
28.30
38.70
17.300
24.800***
34.800***
13.60
25.90
24.30
Source: ibid
33
Table 13: Consumption regressions, 2005 and 2009
2005
Urban
Coefficient
Log of household size
Log of household size
squared
Share
of children 0-6
Share of children 7-16
Share of male adults
Share of female adults
Share of Elderly (>=60)
-0.523***
-0.025**
(dropped)
-0.011
-0.207***
0.024
-0.082**
Metropolitan
Lower Urban
lower RuraL
Upper Urban
Upper Rural
Borders Urban
Borders Rural
(dropped)
-0.216***
(dropped)
-0.213***
(dropped)
0.017
(dropped)
Wage earner
Self employed hiring
others
Self employed working
alone worker
Unpaid
Unemployed
Out of labour force
Out of human force
(dropped)
0.263***
0.057***
-0.040
-0.040
(dropped)
(dropped)
Log of household head's
age
0.172***
Male
Female
(dropped)
-0.010
Illiterate
Can read and write -does
not hold
a degree
Below
average
degree primary-preparatory
Average degree secondary degree equivalent
Above average degree but
below university
University
degreedegree
(dropped)
0.120***
2009
Rural
SE
Coefficient
SE
Household characteristics
0.03
-0.521***
0.02
0.01
0.023***
0.01
(dropped)
0.02
-0.076***
0.02
0.03
-0.307***
0.02
0.03
-0.151***
0.02
0.04
-0.221***
0.02
Region
(dropped)
0.01
(dropped)
0.141***
0.00
0.01
(dropped)
(dropped)
0.02
(dropped)
0.256***
0.02
Custom category
(dropped)
0.01
0.176***
0.01
0.01
0.065***
0.01
0.19
0.207***
0.05
0.07
0.027
0.06
(dropped)
(dropped)
Characteristics of household head
0.02
0.099***
0.01
Gender of the household head
(dropped)
0.02
0.060***
0.01
Education of the household head
(dropped)
0.01
0.081***
0.01
Urban
Coefficient
-0.567***
0.013
(dropped)
-0.126***
-0.235***
-0.045
-0.101**
(dropped)
-0.187***
(dropped)
-0.308***
(dropped)
-0.024
(dropped)
(dropped)
0.273***
0.068***
0.331**
(dropped)
(dropped)
(dropped)
SE
0.03
0.01
0.02
0.03
0.03
0.04
0.01
0.01
0.02
0.01
0.01
0.15
Rural
Coefficient
-0.611***
0.065***
(dropped)
-0.121***
-0.276***
-0.131***
-0.190***
(dropped)
(dropped)
0.176***
(dropped)
(dropped)
(dropped)
0.211***
(dropped)
0.162***
0.063***
0.230***
0.128***
(dropped)
(dropped)
SE
0.02
0.01
0.01
0.02
0.02
0.02
0.00
0.02
0.01
0.01
0.04
0.05
0.170***
0.02
0.106***
0.01
(dropped)
0.015
0.02
(dropped)
0.066***
0.01
(dropped)
0.084***
0.01
(dropped)
0.076***
0.01
0.209***
0.01
0.132***
0.01
0.171***
0.01
0.117***
0.01
0.283***
0.01
0.172***
0.01
0.293***
0.01
0.171***
0.01
0.02
0.01
0.03
0.272***
0.341***
0.615***
0.01
0.01
0.05
0.05
0.05
0.05
(dropped)
(dropped)
0.046***
0.038***
(dropped)
0.377***
0.666***
1.098***
0.02
0.255***
0.01
0.398***
0.01
0.333***
0.01
0.643***
Above university degree
0.03
0.721***
0.06
1.120***
Employment status of the household head
Out of labour force or
(dropped)
(dropped)
(dropped)
human
force
Under employed
(dropped)
(dropped)
-0.100*
Employed normal number
0.020
0.02
0.045***
0.01
-0.055
of days
Over
employed
-0.025
0.02
0.029**
0.01
-0.077
Unemployed
(dropped)
(dropped)
(dropped)
Employment status of the household head
Permanent
(dropped)
(dropped)
(dropped)
Temporary
-0.032***
0.03
-0.025***
0.02
-0.052***
Seasonal
0.052***
0.06
-0.080**
0.04
-0.072***
Custom category
Agriculture
(dropped)
(dropped)
(dropped)
Manufacturing
0.133***
0.02
0.140***
0.01
0.149***
Construction
0.123***
0.02
0.069***
0.01
0.116***
Services
0.116***
0.01
0.092***
0.01
0.123***
Intercept
0.591***
0.06
0.482***
0.04
0.707***
Number of observations
16,145.00
22,192.00
15,729.00
Adjusted R2
0.47
0.39
0.47
Note: *** p<0.01, ** p<0.05, * P<0.1
0.02
0.05
0.01
0.02
0.01
0.08
(dropped)
-0.042***
-0.079***
(dropped)
0.106***
0.059***
0.094***
0.439***
21,336.00
0.42
0.01
0.01
0.01
0.03
0.01
0.01
0.01
0.04
Source: Authors own estimations using Household Income, Expenditure and Consumption Surveys, 2004-05 and
2008-09.
34
Table 14: Probability of being poor, 2005 and 2009
2005
2009
Urban
Rural
Coefficient
SE
Log of household size
Log of household size squared
Share of children 7-16
Share of male adults
Share of female adults
Share of Elderly (>=60)
0.860***
0.273***
0.150
0.808***
0.014
0.060
0.32
0.10
0.14
0.15
0.18
0.31
Lower urban
Lower rural
Upper urban
Upper rural
Borders urban
borders rural
0.250***
0.05
Coefficient
Household characteristics
1.462***
0.046
0.364***
1.252***
0.741***
1.000***
Region
-0.617***
Urban
Rural
SE
Coefficient
SE
0.17
0.05
0.08
0.09
0.11
0.15
1.674***
-0.031
0.938***
0.965***
0.933***
1.044***
0.36
0.11
0.14
0.16
0.18
0.29
0.122**
0.05
0.02
Coefficient
SE
2.172***
-0.226***
0.662***
1.203***
0.667***
1.236***
0.18
0.05
0.08
0.09
0.11
0.14
-0.190*
0.10
0.594***
0.10
0.620***
0.04
0.850***
0.04
-1.003***
0.30
-0.269*
0.14
-0.556***
-0.134***
-0.757
0.06
0.05
0.57
-0.512***
-0.158***
-0.946***
-0.389
0.03
0.04
0.24
0.25
-0.401***
0.11
-0.382***
0.06
-0.076
0.10
-0.222***
0.05
-0.178***
-0.454***
-0.720***
-1.138***
-1.388***
-1.730***
0.05
0.06
0.05
0.12
0.09
0.42
-0.287***
-0.403***
-0.577***
-0.851***
-1.095***
-1.557***
0.03
0.04
0.03
0.07
0.06
0.50
0.239
-0.043
0.011
0.29
0.28
0.28
-0.134**
-0.156***
0.05
0.06
0.164
0.104
0.385***
0.11
0.25
0.06
0.157**
0.292*
0.305***
0.08
0.15
0.04
-0.360***
-0.332***
-0.290***
-2.820***
15,729.00
0.29
0.07
0.08
0.06
0.52
-0.305***
-0.198***
-0.271***
-2.397***
0.04
0.04
0.03
0.25
Self employed hiring others
Self employed working alone
Unpaid worker
Unemployed
-0.464***
-0.084*
1.242*
0.06
0.05
0.69
Log of household head's age
-0.142
0.11
Female
0.139
0.10
Can read and write -does not hold a degree
Below average degree -primary-preparatory
Average degree -secondary degree -equivalent
Above average degree but below university degree
University degree
Above university degree
-0.231***
-0.430***
-0.649***
-0.794***
-1.175***
-6.133
0.05
0.06
0.05
0.10
0.08
Under employed
Employed normal number of days
Over employed
-0.203
-0.529**
-0.374
0.27
0.26
0.27
Temporary
Seasonal
Occasional
0.088
-0.027
0.510***
0.16
0.3
0.07
Manufacturing
Construction
Services
Intercept
Number of observations
Pseudo R2
-0.227***
-0.246***
-0.242***
-2.382***
0.07
0.08
0.06
0.48
16,145.00
0.28
-0.245**
0.10
Custom category
-0.599***
0.03
-0.113***
0.04
-0.783***
0.24
0.105
0.31
Characteristics of household head
-0.358***
0.06
Gender of the household head
-0.139***
0.05
Education of the household head
-0.246***
0.03
-0.401***
0.05
-0.501***
0.03
-0.672***
0.08
-0.928***
0.06
-6.620
Employment status of the household head
-0.187***
0.06
-0.150**
0.06
Employment status of the household head
0.16
0.1
0.025
0.17
0.410***
0.04
Custom category
-0.416***
0.04
-0.182***
0.05
-0.307***
0.03
-1.519***
0.22
22,192.00
0.22
21,336.00
0.24
Source: ibid.
35