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Heba El-Laithy

Arab Development Challenges Background Paper 2011/11

The ADCR 2011: Poverty in Egypt (2009)

Heba El Laithy

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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

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Acronyms and Abbreviations

CAPMAS Central Agency for Public Mobilization and Statistics

FHH Female-headed Household

GDP Gross Domestic Product

HIECS Household Income, Expenditure and Consumption Survey

ILO International Labour Organization

IMF International Monetary Fund

MHH Male-headed Household

SE Standard Deviation

UNDP United Nations Development Programme

1

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.

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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 Percentage change

Change in poverty (P0) 2.46 100

Total Intra-sectoral effect 2.01 81.8

Population-shift effect 0.39 15.73

Interaction effect 0.06 2.46

Intra-sectoral effects:

Urban 0.23 9.19

Rural 1.78 72.61

Source: ibid

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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 between the 96

th and 98

th 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 2005 2009 % change

Urban 3,298.95 3,315.22 0.49

Rural 1,940.34 1,949.79 0.49

Metropolitan 3,983.74 4,000.36 0.42

Lower Urban 2,731.96 2,955.54 8.18

lower Rural 2,115.73 2,172.02 2.66

Upper Urban 2,803.74 2,646.77 -5.60

Upper Rural 1,719.11 1,679.99 -2.28

Total 2,529.48 2,510.48 -0.75

Source: ibid.

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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

Upper half of the distribution

Inter quartile range

Tails Gini

p25/p10 p50/p25 p75/p50 p90/p50 p75/p25 p90/p10

Total

2005 1.29 1.34 1.39 2.05 1.87 3.55 31.85

2009 1.28 1.33 1.38 1.99 1.84 3.4 31.1

Urban

2005 1.32 1.38 1.45 2.26 2 4.1 34.38

2009 1.32 1.38 1.42 2.15 1.96 3.92 33.68

Rural

2005 1.26 1.3 1.3 1.66 1.68 2.71 22.85

2009 1.27 1.28 1.28 1.64 1.63 2.65 22.38

Source: ibid.

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 non-poor 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 2008-09.4 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

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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

Source: Ministry of Economic Development, Follow up report of the economic and social plan performance, 2008-2009

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

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.

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Metropolitan Lower Urban Lower Rural Upper Urban Upper Rural All Egypt

2005 2009

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Table 5: Changes in poverty measurements by region (%), using the lower poverty line, 2005 and 2009

Region Poverty headcount rate Distribution of the poor Distribution of population

2005 2009 Change 2005 2009 Change 2005 2009 Change

Metropolitan 5.7 6.0 0.3 5.4 4.6 -0.8 18.7 17.0 -1.6

Lower Urban 9.0 6.8 -2.2 5.6 3.6 -2.0 12.1 11.5 -0.6

lower Rural 16.7 16.6 -0.1 26.2 24.0 -2.2 30.7 31.8 1.0

Upper Urban 18.6 21.7 3.1 11.3 11.3 0.0 11.9 11.5 -0.4

Upper Rural 39.1 46.1 7.1 50.6 55.8 5.2 25.4 26.6 1.3

All Egypt 19.6 22.0 2.5 100 100 0 100 100 0

Source: ibid.

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

Source: ibid.

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Welfare indicator, units

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2009

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Welfare indicator, units

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2009

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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

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%

0 10 20 30 40 50 60 70

SuezDamiettaPort Said

AlexandriaCairo

GarbeyyaDakahlia

QualiobiaKafr el Sheikh

IsmailiaMenoufia

SharkiaLuxor

BeheiraGiza

FayoumMeniaQena

AswanBani Suef

SohagAssiut

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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

Region Poverty headcount rate (P0) Poverty gap (P1) Squared poverty gap (P2)

2009 2005 Change 2009 2005 Change 2009 2005 Change

Urban -3.29 -3.13 0.16 -3.65 -3.68 -0.04 -3.93 -4.04 -0.12

Rural -2.86 -2.92 -0.06 -3.36 -3.59 -0.23 -3.69 -4.01 -0.32

Total -2.95 -2.97 -0.02 -3.41 -3.61 -0.20 -3.73 -4.02 -0.29

Source: Authors calculations using HIECS, 2004-05 and 2008-09.

Table 7: Elasticity of poverty with respect to inequality, 2005 and 2009

Region Poverty headcount rate (P0) Poverty gap (P1) Squared poverty gap (P2)

2009 2005 Change 2009 2005 Change 2009 2005 Change

Urban 4.74 4.81 0.07 6.26 6.55 0.29 7.28 7.56 0.28

Rural 1.09 1.35 0.25 2.48 2.87 0.39 3.59 4.04 0.45

Total 2.53 2.86 0.32 4.14 4.65 0.50 5.33 5.87 0.54

Source: ibid.

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

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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

(A) (B) (C)

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.

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Table 8: Educational attainment by poverty status in Egypt, 2008-09

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tal

Urban

Non-poor 17.16 14.60 20.17 27.27 4.16 16.08 0.56 100

Poor 33.52 18.95 23.70 19.58 1.40 2.82 0.03 100

Total 18.92 15.06 20.55 26.44 3.87 14.65 0.50 100

Rural

Non-poor 30.63 16.58 20.08 24.66 2.32 5.64 0.09 100

Poor 42.27 16.83 20.92 17.43 0.91 1.64 0.01 100

Total 34.18 16.66 20.34 22.45 1.89 4.42 0.06 100

All Egypt

Non-poor 24.11 15.62 20.13 25.92 3.21 10.69 0.32 100

Poor 40.48 17.27 21.49 17.87 1.01 1.88 0.01 100

Total 27.74 15.99 20.43 24.14 2.72 8.73 0.25 100

Source: ibid.

Table 9: Poverty measurements by educational attainment in Egypt, 2008-09

Illiterate

Can read and write

Primary-preparatory

Secondary degree or equivalent

Above average degree

University degree

Above university

degree Total

Urban

P0 19.04 13.51 12.39 7.96 3.90 2.07 0.56 10.75

P1 3.56 2.41 2.19 1.32 0.63 0.27 0.08 1.90

P2 1.07 0.67 0.62 0.34 0.17 0.06 0.01 0.54

Rural

P0 37.72 30.82 31.38 23.68 14.64 11.29 3.21 30.50

P1 7.86 5.85 6.09 4.34 2.54 1.88 0.53 6.00

P2 2.45 1.72 1.82 1.24 0.70 0.51 0.09 1.81

All Egypt

P0 32.35 23.94 23.32 16.41 8.21 4.77 0.96 22.17

P1 6.62 4.48 4.43 2.95 1.40 0.74 0.15 4.27

P2 2.05 1.30 1.31 0.82 0.39 0.19 0.02 1.27

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

Source: ibid.

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urban Rural

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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 poverty-reduction 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 self-employment; (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.

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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

Participation rate Unemployment rate

Male Female Total Male Female Total

Urban

Non-poor 77.78 27.17 52.23 4.54 12.13 6.53

Poor 78.37 24.02 51.86 5.57 18.98 8.60

Total 77.84 26.85 52.19 4.65 12.75 6.74

Rural

Non-poor 82.85 46.86 64.65 1.90 6.47 3.58

Poor 80.78 39.45 60.99 2.14 6.94 3.63

Total 82.21 44.73 63.56 1.97 6.59 3.59

All Egypt

Non-poor 80.37 37.23 58.57 3.15 8.49 4.87

Poor 80.29 36.21 59.1 2.83 8.62 4.53

Total 80.35 37.02 58.68 3.08 8.52 4.79

Source: ibid.

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 under-employed. 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

13

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 Temporary Seasonal Occasional Total

Urban

Non poor 86.37 6.21 0.41 7.01 100

Poor 73.08 6.41 1.07 19.44 100

Total 85.01 6.23 0.47 8.29 100

Rural

Non poor 87.04 3.67 0.44 8.85 100

Poor 74.68 3.58 0.77 20.97 100

Total 83.49 3.64 0.54 12.34 100

All Egypt

Non poor 86.76 4.75 0.43 8.07 100

Poor 74.41 4.07 0.82 20.70 100

Total 84.05 4.60 0.51 10.84 100

Source: ibid.

Table 12: Poverty measurements by job regularity in Egypt, 2008-09

Permanent Temporary Seasonal Occasional Total

Urban

P0 8.83 10.60 23.31 24.09 10.28

P1 1.45 1.62 4.45 4.58 1.73

P2 0.39 0.42 1.45 1.33 0.47

Rural

P0 25.74 28.27 41.26 48.89 28.77

P1 4.53 5.34 7.29 11.15 5.39

P2 1.24 1.58 1.79 3.64 1.55

All Egypt

P0 19.43 19.43 35.13 41.90 21.94

P1 3.38 3.48 6.32 9.30 4.04

P2 0.92 1.00 1.67 2.98 1.15

Source: ibid

Table 13: Rate of under and over employment by poverty status, 2008-09

Rate of under employment Rate of over employment

Urban Rural Total Urban Rural Total

Males

Non poor 5.26 7.07 6.14 8.35 10.88 9.57

Poor 11.66 14.01 13.51 13.87 11.27 11.82

Total 5.93 9.26 7.77 8.93 11.00 10.07

Females

Non poor 3.30 7.30 4.57 3.12 4.43 3.54

Poor 11.03 21.09 17.36 5.98 4.39 4.98

Total 3.71 9.62 5.76 3.27 4.42 3.67

All Egypt

Non poor 4.77 7.11 5.82 7.03 9.96 8.35

Poor 11.57 14.49 13.83 12.78 10.80 11.25

Total 5.40 9.30 7.41 7.57 10.21 8.93

Source: ibid

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,

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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 Self employed Unpaid worker Unemployed Total

Urban

Non poor 68.97 8.86 11.82 3.82 6.53 100

Poor 60.90 6.06 15.13 9.32 8.59 100

Total 68.13 8.57 12.16 4.39 6.74 100

Rural

Non poor 43.81 14.79 17.50 20.31 3.58 100

Poor 46.18 11.99 13.89 24.31 3.63 100

Total 44.49 13.99 16.47 21.46 3.59 100

All Egypt

Non poor 54.79 12.20 15.02 13.11 4.87 100

Poor 48.85 10.91 14.12 21.59 4.53 100

Total 53.51 11.92 14.82 14.95 4.79 100

Source: ibid

Table 15: Poverty measurements by type of work, 2008-09

Wage earner Employers Self employed Unpaid worker Unemployed Total

Urban

P0 9.24 7.31 12.87 21.95 13.19 10.34

P1 1.57 1.06 2.25 3.44 2.46 1.75

P2 0.43 0.26 0.63 0.87 0.72 0.48

Rural

P0 29.77 24.57 24.20 32.49 28.99 28.68

P1 5.95 4.21 4.42 5.63 5.97 5.38

P2 1.80 1.11 1.26 1.51 1.85 1.55

All Egypt

P0 19.80 19.84 20.65 31.31 20.51 21.69

P1 3.82 3.35 3.74 5.38 4.09 4.00

P2 1.14 0.88 1.06 1.44 1.24 1.14

Source: ibid

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%.

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Table 16: The incidence of poverty by type of work, 2008-09

Agricultural activity Non agricultural activity

Total Wage worker

Employer Self

employed Unpaid worker

Wage worker

Employer Self

employed Unpaid worker

Urban 28.24 18.78 15.83 27.00 8.84 4.22 11.73 12.36 10.28

Rural 49.58 26.11 24.04 32.96 25.44 17.78 24.51 30.56 28.77

All Egypt 46.95 25.42 22.93 32.46 16.63 10.08 17.96 23.61 21.94

Source: ibid

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 self-employed 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.

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Table 17: Labour force participation and unemployment rates among youth, 2008-09

Age Participation rate Unemployment rate

Poor Non poor Total Poor Non poor Total

15 16.99 9.27 11.53 poor non poor Total

16 27.26 13.20 17.22 2.21 3.70 3.06

17 30.41 18.63 22.00 4.22 3.48 3.81

18 44.94 33.12 36.31 9.52 12.79 11.50

19 53.65 37.42 41.85 13.65 18.36 16.79

20 49.10 39.70 42.24 13.60 19.27 17.28

21 49.87 41.80 43.81 12.94 16.21 15.18

22 55.15 52.56 53.22 13.21 20.62 18.52

23 67.49 61.29 62.65 14.66 19.98 18.58

24 66.91 65.52 65.83 12.23 15.74 14.91

25 64.86 63.13 63.52 9.53 13.74 12.80

26 69.72 69.04 69.17 5.00 7.82 7.18

27 68.14 68.79 68.66 5.71 8.70 8.12

28 68.87 67.57 67.80 3.35 5.97 5.46

29 65.17 68.01 67.58 3.65 3.88 3.84

All youth 52.68 48.78 49.71 2.16 4.04 3.76

Source: ibid

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

Secondary Above secondary University or higher education

Poor 16.05 28.67 29.37

Non-poor 14.27 21.27 25.27

All youth 14.71 22.26 25.62

Source: ibid

Employment in rural areas

Agriculture and non-agriculture employment linkages Table 19: Employment status by expenditure quintiles in rural Egypt, 2008-09

1

st Quintile

2nd

Quintile

3rd

Quintile

4th

Quintile

5th

Quintile

All rural

Wage worker in agriculture 17.26 10.63 8.42 6.03 3.63 8.64

Self employed in agriculture 21.69 23.81 24.07 23.84 25.84 24.00

Unpaid worker in agriculture 20.75 22.93 22.70 20.41 15.72 20.31

Agricultural Employment Total 59.7 57.37 55.19 50.28 45.19 52.95

Wage worker not in agriculture 31.20 33.61 36.13 38.85 41.23 36.62

Self employed not in agriculture 7.55 7.69 7.74 9.29 12.09 9.04

Unpaid worker not in agriculture 1.55 1.32 0.94 1.58 1.49 1.38

Non-Agricultural Employment Total 40.3 42.63 44.81 49.72 54.81 47.04

Source: ibid.

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

17

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 non-agricultural 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 non-agricultural 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 non-agricultural 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

Male Female All rural

Wage worker in agriculture 11.41 3.06 8.64

Self employed in agriculture 18.67 34.72 24.00

Unpaid worker in agriculture 9.54 42.00 20.31

Agricultural Employment Total 39.62 79.78 52.95

Wage worker not in agriculture 48.15 13.42 36.63

Self employed not in agriculture 10.92 5.26 9.04

Unpaid worker not in agriculture 1.30 1.53 1.38

Non-Agricultural Employment Total 60.38 20.21 47.05

Source: ibid.

As previously demonstrated, the distribution of the working population across quintiles revealed that the richer quintiles clustered out of the agricultural sector into the non-agricultural 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 non-agricultural sector. Educational attainment seems to be an important determinant of

18

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

Illiterate

Read and write

Basic education

Secondary education

University education

All rural

Wage worker in agriculture 10.19 8.21 9.78 7.62 2.57 8.64

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

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 non-agricultural 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

1

st Quintile

2nd

Quintile

3rd

Quintile

4th

Quintile

5th

Quintile

All rural

Income from wages 45.03 40.17 38.52 36.70 30.62 36.43

Income from agricultural wages 13.73 7.90 6.02 3.88 1.84 5.25

Income from agricultural self employed activities 24.62 28.70 29.64 28.27 24.52 26.82

Total Agricultural sources of income 38.35 36.6 35.66 32.15 26.36 32.07

Income from non agricultural wages 31.31 32.27 32.50 32.82 28.78 31.07

Income from non-agricultural self employed activities 9.24 10.10 10.13 12.24 14.19 11.87

Total Non-Agricultural sources of income 40.55 42.37 42.63 45.06 42.97 42.94

Income from financial assets 0.05 0.14 0.26 0.32 0.92 0.46

Income from real estate with imputed rent 0.53 0.90 1.15 1.50 4.28 2.19

Income from transfers 10.63 9.71 10.05 10.72 15.54 12.08

Total imputed rent for all household 9.89 10.29 10.25 10.25 9.94 10.11

Total non-labour sources of income 21.10 21.04 21.71 22.79 30.68 24.84

Total household income with imputed rent 100 100 100 100 100 100 Source: ibid.

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)

19

decreases sharply in the richer quintiles (from 38% in the 1st quintile to 26% in the 5th 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

Average number of adults

Average number of elderly

Average household size

Urban

Non-poor 1.20 2.52 0.34 4.07

Poor 1.99 3.85 0.34 6.18

Total 1.26 2.62 0.34 4.22

Rural

Non-poor 1.60 2.65 0.33 4.58

Poor 2.37 3.85 0.38 6.61

Total 1.78 2.92 0.34 5.04

All Egypt

Non-poor 1.40 2.59 0.33 4.32

Poor 2.29 3.85 0.37 6.52

Total 1.54 2.79 0.34 4.67

Source: ibid.

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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 non-poor, 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 4 persons 5

persons 6 or 7

persons 8 persons or

more Total

Urban

P0 0.24 1.14 1.91 3.37 7.22 17.65 39.25 10.58

P1 0.01 0.19 0.22 0.48 1.01 3.08 8.40 1.87

P2 0.00 0.06 0.05 0.10 0.24 0.84 2.70 0.53

Rural

P0 2.35 4.89 8.26 12.47 19.74 36.04 54.21 29.99

P1 0.47 0.90 1.23 1.76 3.06 6.75 12.38 5.89

P2 0.16 0.25 0.31 0.42 0.76 1.94 4.08 1.77

All Egypt

P0 1.16 2.84 4.99 7.84 13.82 29.40 51.15 22.02

P1 0.22 0.51 0.71 1.11 2.09 5.42 11.57 4.24

P2 0.07 0.14 0.17 0.26 0.51 1.54 3.80 1.26

Source: ibid.

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, 2008-09

P0 P1 P2 Contribution to poverty Population Share

Urban

Male 7.58 1.26 0.34 87.65 83.59

Female 5.44 0.99 0.28 12.35 16.41

Total 7.23 1.21 0.33 100 100

Rural

Male 24.33 4.51 1.30 88.48 83.22

Female 15.70 3.01 0.90 11.52 16.78

Total 22.88 4.25 1.23 100 100

All Egypt

Male 16.70 3.03 0.86 88.31 83.39

Female 11.10 2.10 0.62 11.69 16.61

Total 15.77 2.87 0.82 100 100

Source: ibid.

21

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

Income from

agricultural activities

Income from non agricultural

projects

Return from financial assets

Income from real estate property

Transfers Total

income

Male 49.09 16.92 19.89 1.24 2.21 10.65 100

Female 30.68 10.37 7.18 1.17 3.27 47.34 100

Total 46.79 16.10 18.30 1.23 2.34 15.23 100

Source: ibid.

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 non-poor 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

22

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 Girls Total

Urban

Non-poor 3.6 2.86 3.23

Poor 13.4 9.87 11.73

Total 5.14 3.9 4.54

Rural

Non-poor 4.76 6.18 5.45

Poor 12.79 17.76 15.15

Total 7.73 10.32 8.98

All Egypt

Non-poor 4.25 4.73 4.48

Poor 12.91 16.21 14.47

Total 6.78 7.94 7.34

Source: ibid.

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, 2008-09

Source: ibid.

0

10

20

30

40

Non poor Poor Total

Male headed Female headed All households

23

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 Urban

Non-poor 95.62 95.76 95.69

Poor 85.08 86.1 85.57

Total 94.17 94.49 94.32

Rural

Non-poor 93.84 93.39 93.62

Poor 87.12 81.9 84.61

Total 91.54 89.52 90.56

All Egypt

Non-poor 94.62 94.45 94.54

Poor 86.72 82.72 84.8

Total 92.52 91.42 91.98

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

Urban

Non-poor 81.59 81.23 81.41

Poor 52.74 59.81 56.04

Total 76.88 77.92 77.38

Rural

Non-poor 75 69.26 72.11

Poor 59.57 49.75 54.97

Total 69.05 62.36 65.77

All Egypt

Non-poor 77.93 74.34 76.16

Poor 58.21 51.74 55.18

Total 71.95 67.98 70.02

Source: ibid.

24

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

Urban

Non-poor 9.96 1.96 6.04

Poor 26.43 4.38 16

Total 12.55 2.32 7.57

Rural

Non-poor 14.96 3.04 9.15

Poor 23.86 5.18 15.02

Total 18.26 3.81 11.28

All Egypt

Non-poor 12.78 2.57 7.79

Poor 24.37 5.02 15.21

Total 16.17 3.26 9.92

Source: ibid.

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

25

“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 non-poor. 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.

26

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

Use improved fuel for cooking

improved wall

materials

improved sewerage disposals

improved garbage collection facilities

Urban

Non-poor 99.73 99.87 99.72 98.63 95.37 84.22

Poor 99.04 98.76 98.3 90.68 86.09 59.78

Total 99.66 99.76 99.57 97.79 94.39 82.46

Rural

Non-poor 98.95 99.47 99.14 91.19 90.24 29.56

Poor 98.07 98.4 96.3 82.53 83.95 14.52

Total 98.69 99.15 98.29 88.59 88.35 26.12

All Egypt

Non-poor 99.32 99.66 99.41 94.69 92.66 56.91

Poor 98.26 98.47 96.7 84.14 84.38 23.93

Total 99.09 99.4 98.81 92.37 90.83 51.71

Source: ibid.

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,

27

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 university-educated 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.

28

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.

29

Endnotes 1 World Bank 2002

2 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

30

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.

31

ANNEX TABLES

Table 1: Poverty measures by governorates, 2008/09

Governorate P0 P1 P2

Urban Rural Total Urban Rural Total Urban Rural Total

Metropolitan

Cairo 7.62

7.62 1.27

1.27 0.34

0.34

Alexandria 6.41

6.41 1.02

1.02 0.27

0.27

Port Said 4.43

4.43 0.93

0.93 0.31

0.31

Suez 1.94

1.94 0.28

0.28 0.08

0.08

Lower Egypt

Damietta 1.20 1.05 1.11 0.23 0.11 0.16 0.06 0.02 0.03

Dakahlia 4.82 10.99 9.30 0.66 1.28 1.11 0.13 0.25 0.22

Sharkia 10.76 21.61 19.15 1.16 2.84 2.46 0.18 0.59 0.50

Qualiobia 3.43 16.83 11.33 0.46 2.52 1.68 0.11 0.59 0.40

Kafr el Sheikh 5.56 12.61 11.20 0.63 1.99 1.72 0.08 0.48 0.40

Garbeyya 5.24 8.60 7.64 0.54 1.02 0.88 0.09 0.20 0.17

Menoufia 9.39 19.90 17.93 1.26 2.91 2.60 0.27 0.66 0.59

Beheira 17.64 24.87 23.51 2.60 3.56 3.38 0.66 0.80 0.77

Ismailia 12.14 23.90 18.84 1.76 4.06 3.07 0.39 1.06 0.77

Upper Egypt

Giza 11.84 37.69 22.97 2.17 8.26 4.79 0.61 2.63 1.48

Bani Suef 33.44 44.03 41.45 5.66 8.07 7.48 1.42 2.22 2.03

Fayoum 15.85 32.12 28.71 2.20 5.48 4.79 0.50 1.36 1.18

Menia 16.13 33.73 30.93 2.89 5.97 5.48 0.71 1.58 1.44

Assiut 38.65 68.21 60.97 9.09 19.16 16.70 3.08 7.14 6.15

Sohag 32.99 51.34 47.54 7.91 11.53 10.78 2.62 3.73 3.50

Qena 34.82 40.12 39.02 7.80 8.08 8.03 2.48 2.53 2.52

Aswan 28.72 49.60 40.92 6.21 11.03 9.03 2.04 3.44 2.85

luxor 8.39 28.33 18.44 1.22 5.31 3.28 0.25 1.74 1.00

Border regions

Red Sea 1.46 43.57 4.00 0.48 8.82 0.98 0.17 2.18 0.29

New Valley 0.00 11.64 6.24 0.00 1.34 0.72 0.00 0.21 0.11

Matrouh 1.17 10.95 4.32 0.07 1.91 0.66 0.00 0.36 0.12

North Sinai 15.61 44.91 27.87 2.81 9.39 5.56 0.68 2.59 1.48

South Sinai 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

All Egypt 10.98 28.94 21.56 1.97 5.58 4.10 0.56 1.66 1.20

Source: Author’s calculations using HIECS 2008-09.

Table 2: Poverty by household head's status of employment, 2005 and 2009

Poverty headcount rate Distribution of the poor Distribution of population

2005 2009 Change 2005 2009 Change 2005 2009 Change

Wage earner 18.8 20.7 2.0 48.4 45.6 -2.8 50.5 48.4 -2.1

Self employed hiring others 22.4 25.9 3.6 28.0 27.8 -0.3 24.5 23.6 -0.9

Self employed working alone 21.4 21.9 0.5 12.2 11.4 -0.8 11.2 11.5 0.3

Unpaid worker 29.5 32.6 3.1 0.2 0.2 0.1 0.1 0.2 0.1

Unemployed 21.6 21.4 -0.2 0.3 0.3 -0.0 0.2 0.3 0.0

Out of labour force 16.0 19.1 3.2 6.8 8.3 1.5 8.3 9.6 1.2

Out of human force 15.7 21.7 5.9 4.1 6.5 2.3 5.1 6.6 1.4

Total 19.6 22.0 2.5 100.0 100.0 0.0 100.0 100.0 0.0

Source: Author’s calculations using HIECS 2008-09 and Hiecs 2004-05

Table 3: Poverty by education level, 2005 and 2009

Poverty headcount rate Distribution of the poor Distribution of population

2005 2009 Change 2005 2009 Change 2005 2009 Change

Illiterate 28.5 32.3 3.9 41.2 40.5 -0.7 28.6 27.7 -0.8

Can read and write -does not hold a degree

20.8 23.9 3.2 17.5 17.3 -0.2 16.6 16.0 -0.6

Below average degree -primary-preparatory

20.9 23.3 2.4 21.4 21.5 0.1 20.2 20.4 0.2

Average degree -secondary degree -equivalent

14.3 16.4 2.1 16.7 17.9 1.2 23.1 24.1 1.0

Above average degree but below university degree

8.6 8.2 -0.4 1.2 1.0 -0.2 2.7 2.7 0.0

University degree 4.7 4.8 0.0 2.1 1.9 -0.2 8.6 8.7 0.2

Above university degree 0.2 1.0 0.7 0.0 0.0 0.0 0.3 0.2 -0.0

Total 19.6 22.0 2.5 100.0 100.0 0.0 100.0 100.0 0.0

Source: ibid

32

Table 4: Poverty by household head's education level, 2005 and 2009

Poverty headcount rate Distribution of the poor Distribution of population

2005 2009 Change 2005 2009 Change 2005 2009 Change

Illiterate 31.3 35.5 4.1 57.7 58.5 0.8 36.0 36.3 0.3

Can read and write -does not hold a degree

21.6 24.0 2.5 21.6 17.8 -3.8 19.6 16.3 -3.3

Below average degree -primary-preparatory

14.2 17.3 3.0 6.1 8.5 2.4 8.4 10.8 2.4

Average degree -secondary degree -equivalent

10.8 12.6 1.8 11.2 12.1 0.9 20.3 21.2 0.9

Above average degree but below university degree

7.6 7.9 0.3 1.4 1.4 -0.0 3.6 3.8 0.2

University degree 3.4 3.3 -0.1 2.0 1.7 -0.4 11.6 11.0 -0.6

Above university degree 0.0 1.2 1.2 0.0 0.0 0.0 0.5 0.5 -0.0

Total 19.6 22.0 2.5 100.0 100.0 0.0 100.0 100.0 0.0

Source: ibid

Table 5: Poverty by household head's gender, 2005 and 2009

Poverty headcount rate Distribution of the poor Distribution of population

2005 2009 Change 2005 2009 Change 2005 2009 Change

Male 20.1 22.4 2.4 91.7 89.8 -1.9 89.5 88.1 -1.4

Female 15.3 18.9 3.5 8.3 10.2 1.9 10.5 11.9 1.4

Total 19.6 22.0 2.5 100.0 100.0 0.0 100.0 100.0 0.0

Source: ibid

Table 6: Poverty by demographic composition, 2005 and 2009

Poverty headcount rate Distribution of the poor Distribution of population

2005 2009 Change 2005 2009 Change 2005 2009 Change

no children 18.0 19.2 1.2 46.1 40.3 -5.7 50.1 46.2 -3.8

1 19.3 21.7 2.4 25.6 25.9 0.3 25.9 26.2 0.4

2 20.6 22.4 1.8 18.9 19.8 0.8 18.0 19.5 1.5

3 or more children 30.2 38.1 7.8 9.4 14.0 4.6 6.1 8.1 2.0

Household size

1 0.6 1.2 0.5 0.0 0.1 0.0 1.5 1.1 -0.4

2 3.0 2.8 -0.1 0.8 0.6 -0.2 5.2 4.3 -1.0

3 5.2 5.0 -0.2 2.7 2.0 -0.7 10.1 8.8 -1.3

4 7.6 7.8 0.3 7.3 6.5 -0.8 18.9 18.3 -0.6

5 12.8 13.8 1.0 15.2 14.2 -1.0 23.3 22.7 -0.6

6 23.3 24.5 1.2 21.3 18.9 -2.4 17.9 17.0 -0.9

7 or more 44.5 45.6 1.1 52.7 57.8 5.1 23.2 27.9 4.8

Total 19.6 22.0 2.5 100.0 100.0 0.0 100.0 100.0 0.0

Source: ibid

Table 7: Poverty by sector of employment, 2005 and 2009

Poverty headcount rate Distribution of the poor Distribution of population

2005 2009 Change 2005 2009 Change 2005 2009 Change

Government 10.4 11.3 0.9 11.4 9.9 -1.5 20.9 19.2 -1.8

Public 9.0 8.8 -0.2 1.7 1.2 -0.5 3.6 3.1 -0.6

Private 14.4 15.4 1.0 22.4 21.4 -1.1 29.7 30.4 0.7

Outside establishment 26.9 31.3 4.4 64.4 67.0 2.7 45.6 46.9 1.3

Others 5.9 20.3 14.4 0.1 0.5 0.4 0.2 0.5 0.3

Total 19.6 22.0 2.5 100.0 100.0 0.0 100.0 100.0 0.0

Source: ibid

Table 8: Poverty by sector of employment of household head, 2005 and 2009

Poverty headcount rate Distribution of the poor Distribution of population

2005 2009 Change 2005 2009 Change 2005 2009 Change

Government 16.2 17.1 0.9 21.5 19.1 -2.4 26.8 25.0 -1.7

Public 13.1 11.0 -2.1 3.9 2.3 -1.6 6.0 4.6 -1.4

Private 13.7 14.3 0.6 20.9 18.9 -1.9 30.8 29.7 -1.1

Outside establishment 29.9 33.0 3.1 53.6 59.2 5.5 36.2 40.1 3.9

Others 8.8 22.0 13.2 0.1 0.5 0.4 0.3 0.5 0.3

Total 19.6 22.0 2.5 100.0 100.0 0.0 100.0 100.0 0.0

Source: ibid

33

Table 9: Poverty by economic activity, 2005 and 2009

Economic activity Poverty headcount rate Distribution of the poor Distribution of population

2005 2009 Change 2005 2009 Change 2005 2009 change

Agriculture 26.8 31.1 4.4 20.0 19.1 -0.9 14.6 13.5 -1.1

Mining 9.2 12.7 3.6 0.0 0.1 0.1 0.1 0.1 0.1

Manufacturing 14.6 17.1 2.4 3.0 3.0 0.0 4.0 3.8 -0.1

Electricity 9.2 10.1 1.0 0.1 0.2 0.0 0.3 0.4 0.1

Construction 24.7 29.4 4.7 3.3 4.3 0.9 2.6 3.2 0.6

Trade, hotel and restrant 15.4 16.8 1.3 3.7 3.5 -0.2 4.7 4.6 -0.1

Transport 11.7 17.0 5.3 1.4 1.5 0.1 2.3 1.9 -0.4

Finance and real estate 4.9 6.2 1.3 0.1 0.2 0.0 0.6 0.6 -0.0

Public and family services 10.9 11.8 0.8 4.4 4.3 -0.1 7.9 8.0 0.2

Not applicable 19.9 22.1 2.2 63.9 63.9 0.0 62.9 63.7 0.8

Total 19.6 22.0 2.5 100.0 100.0 0.0 100.0 100.0 0.0

Source: ibid

Table 10: Poverty by the status of employment, 2005 and 2009

Employment status Poverty headcount rate Distribution of the poor Distribution of population

2005 2009 Change 2005 2009 Change 2005 2009 Change

Severely under employed 34.0 30.4 -3.6 0.8 0.9 0.0 0.5 0.6 0.2

Under employed 33.7 35.8 2.1 1.7 2.3 0.5 1.0 1.4 0.4

Employed_normal working days 15.9 18.4 2.6 17.2 17.7 0.5 21.1 21.1 0.0

Over employed 22.2 25.7 3.5 16.4 15.2 -1.2 14.3 13.0 -1.4

Unemployed 25.2 20.8 -4.4 2.8 1.7 -1.1 2.1 1.8 -0.4

Out of labour force 20.8 23.0 2.2 46.9 46.5 -0.4 43.8 44.5 0.7

Out of human force 16.0 19.8 3.8 14.2 15.8 1.6 17.2 17.6 0.4

Total 19.4 22.0 2.6 100.0 100.0 0.0 100.0 100.0 0.0

Source: ibid

Table 11: Poverty by Household Head's Status of Employment, 2005 and 2009

Employment status of the household head

Poverty headcount rate Distribution of the poor Distribution of population

2005 2009 Change 2005 2009 Change 2005 2009 Change

Severely under employed 25.6 28.3 2.7 1.1 1.4 0.3 0.9 1.1 0.3

Under employed 34.0 33.9 -0.1 3.5 3.9 0.4 2.0 2.5 0.5

Employed normal working days 17.3 19.5 2.2 50.5 48.5 -2.0 56.7 54.7 -2.1

Over employed 24.6 27.2 2.5 33.3 31.2 -2.1 26.3 25.3 -1.0

Unemployed 15.8 21.4 5.6 0.2 0.3 0.1 0.2 0.3 0.0

Out of labour force 15.9 19.1 3.2 7.1 8.3 1.2 8.6 9.6 0.9

Out of human force 16.0 21.7 5.7 4.3 6.5 2.2 5.2 6.6 1.3

Total 19.4 22.0 2.6 100.0 100.0 0.0 100.0 100.0 0.0

Source: ibid

Table 12: Changes in the probability of being in poverty (%), 2005 and 2009

2005 2009

Urban Rural Urban Rural

Demographic event, child born in the family:

Change from having no children 0-6 years old to having 1 child 28 12 21 8

Change from having no children 0-6 years old to having 2 children 60.800*** 26.90 44.500*** 18.10

Change of household head (i.e., followed from a divorce, migration, etc.):

change from "Male" to "Female" 5.700 -14.90 -4.100*** -17.20

Education event, change in household's head education:

change from "illiterate" to "can read and write -does not hold a degree" -29.400** -21.70 -21.700*** -19.90

change from "illiterate" to "below average degree -primary-preparatory" -47.400*** -34.40 -41.000*** -29.50

change from "illiterate" to "average degree -secondary degree -equivalent" -60 -43 -62 -42

change from "illiterate" to "above average degree but below university degree" -71.500 -59.50 -75.400 -61.30

change from "illiterate" to "university degree" -92.600*** -71.90 -92.500 -72.00

change from "illiterate" to "above university degree" -99.400 -97.50 -99.600*** -94.30

Sector of employment event, household head's sector of employment:

change from "permanent" to "temporary" 10.400 8.50 17.300 13.60

change from "permanent" to "seasonal" -15.300 28.30 24.800*** 25.90

change from "permanent" to "occasional" 39.000 38.70 34.800*** 24.30

Source: ibid

34

Table 13: Consumption regressions, 2005 and 2009

2005 2009

Urban Rural Urban Rural

Coefficient SE Coefficient SE Coefficient SE Coefficient SE

Household characteristics

Log of household size -0.523*** 0.03 -0.521*** 0.02 -0.567*** 0.03 -0.611*** 0.02

Log of household size squared

-0.025** 0.01 0.023*** 0.01 0.013 0.01 0.065*** 0.01

Share of children 0-6 (dropped)

(dropped)

(dropped)

(dropped)

Share of children 7-16 -0.011 0.02 -0.076*** 0.02 -0.126*** 0.02 -0.121*** 0.01

Share of male adults -0.207*** 0.03 -0.307*** 0.02 -0.235*** 0.03 -0.276*** 0.02

Share of female adults 0.024 0.03 -0.151*** 0.02 -0.045 0.03 -0.131*** 0.02

Share of Elderly (>=60) -0.082** 0.04 -0.221*** 0.02 -0.101** 0.04 -0.190*** 0.02

Region

Metropolitan (dropped)

(dropped)

(dropped)

(dropped)

Lower Urban -0.216*** 0.01 (dropped)

-0.187*** 0.01 (dropped)

lower RuraL (dropped)

0.141*** 0.00 (dropped)

0.176*** 0.00

Upper Urban -0.213*** 0.01 (dropped)

-0.308*** 0.01 (dropped)

Upper Rural (dropped)

(dropped)

(dropped)

(dropped)

Borders Urban 0.017 0.02 (dropped)

-0.024 0.02 (dropped)

Borders Rural (dropped)

0.256*** 0.02 (dropped)

0.211*** 0.02

Custom category

Wage earner (dropped)

(dropped)

(dropped)

(dropped)

Self employed hiring others

0.263*** 0.01 0.176*** 0.01 0.273*** 0.01 0.162*** 0.01

Self employed working alone

0.057*** 0.01 0.065*** 0.01 0.068*** 0.01 0.063*** 0.01

Unpaid worker -0.040 0.19 0.207*** 0.05 0.331** 0.15 0.230*** 0.04

Unemployed -0.040 0.07 0.027 0.06 (dropped)

0.128*** 0.05

Out of labour force (dropped)

(dropped)

(dropped)

(dropped)

Out of human force (dropped)

(dropped)

(dropped)

(dropped)

Characteristics of household head

Log of household head's age

0.172*** 0.02 0.099*** 0.01 0.170*** 0.02 0.106*** 0.01

Gender of the household head

Male (dropped)

(dropped)

(dropped)

(dropped)

Female -0.010 0.02 0.060*** 0.01 0.015 0.02 0.066*** 0.01

Education of the household head

Illiterate (dropped)

(dropped)

(dropped)

(dropped)

Can read and write -does not hold a degree

0.120*** 0.01 0.081*** 0.01 0.084*** 0.01 0.076*** 0.01

Below average degree -primary-preparatory

0.209*** 0.01 0.132*** 0.01 0.171*** 0.01 0.117*** 0.01

Average degree -secondary degree -equivalent

0.283*** 0.01 0.172*** 0.01 0.293*** 0.01 0.171*** 0.01

Above average degree but below university degree

0.377*** 0.02 0.255*** 0.01 0.398*** 0.02 0.272*** 0.01

University degree 0.666*** 0.01 0.333*** 0.01 0.643*** 0.01 0.341*** 0.01

Above university degree 1.098*** 0.03 0.721*** 0.06 1.120*** 0.03 0.615*** 0.05

Employment status of the household head

Out of labour force or human force

(dropped)

(dropped)

(dropped)

(dropped)

Under employed (dropped)

(dropped)

-0.100* 0.05 (dropped)

Employed normal number of days

0.020 0.02 0.045*** 0.01 -0.055 0.05 0.046*** 0.01

Over employed -0.025 0.02 0.029** 0.01 -0.077 0.05 0.038*** 0.01

Unemployed (dropped)

(dropped)

(dropped)

(dropped)

Employment status of the household head

Permanent (dropped)

(dropped)

(dropped)

(dropped)

Temporary -0.032*** 0.03 -0.025*** 0.02 -0.052*** 0.02 -0.042*** 0.01

Seasonal 0.052*** 0.06 -0.080** 0.04 -0.072*** 0.05 -0.079*** 0.03

Custom category

Agriculture (dropped)

(dropped)

(dropped)

(dropped)

Manufacturing 0.133*** 0.02 0.140*** 0.01 0.149*** 0.01 0.106*** 0.01

Construction 0.123*** 0.02 0.069*** 0.01 0.116*** 0.02 0.059*** 0.01

Services 0.116*** 0.01 0.092*** 0.01 0.123*** 0.01 0.094*** 0.01

Intercept 0.591*** 0.06 0.482*** 0.04 0.707*** 0.08 0.439*** 0.04

Number of observations 16,145.00 22,192.00 15,729.00 21,336.00

Adjusted R2 0.47 0.39 0.47 0.42

Note: *** p<0.01, ** p<0.05, * P<0.1

Source: Authors own estimations using Household Income, Expenditure and Consumption Surveys, 2004-05 and 2008-09.

35

Table 14: Probability of being poor, 2005 and 2009

2005 2009

Urban Rural Urban Rural

Coefficient SE Coefficient SE Coefficient SE Coefficient SE

Household characteristics

Log of household size 0.860*** 0.32 1.462*** 0.17 1.674*** 0.36 2.172*** 0.18

Log of household size squared 0.273*** 0.10 0.046 0.05 -0.031 0.11 -0.226*** 0.05

Share of children 7-16 0.150 0.14 0.364*** 0.08 0.938*** 0.14 0.662*** 0.08

Share of male adults 0.808*** 0.15 1.252*** 0.09 0.965*** 0.16 1.203*** 0.09

Share of female adults 0.014 0.18 0.741*** 0.11 0.933*** 0.18 0.667*** 0.11

Share of Elderly (>=60) 0.060 0.31 1.000*** 0.15 1.044*** 0.29 1.236*** 0.14

Region

Lower urban 0.250*** 0.05

0.122** 0.05

Lower rural

-0.617*** 0.02

-0.190* 0.10

Upper urban 0.620*** 0.04

0.850*** 0.04

Upper rural

0.594*** 0.10

Borders urban -1.003*** 0.30

-0.269* 0.14

borders rural

-0.245** 0.10

Custom category

Self employed hiring others -0.464*** 0.06 -0.599*** 0.03 -0.556*** 0.06 -0.512*** 0.03

Self employed working alone -0.084* 0.05 -0.113*** 0.04 -0.134*** 0.05 -0.158*** 0.04

Unpaid worker 1.242* 0.69 -0.783*** 0.24 -0.757 0.57 -0.946*** 0.24

Unemployed

0.105 0.31

-0.389 0.25

Characteristics of household head

Log of household head's age -0.142 0.11 -0.358*** 0.06 -0.401*** 0.11 -0.382*** 0.06

Gender of the household head

Female 0.139 0.10 -0.139*** 0.05 -0.076 0.10 -0.222*** 0.05

Education of the household head

Can read and write -does not hold a degree -0.231*** 0.05 -0.246*** 0.03 -0.178*** 0.05 -0.287*** 0.03

Below average degree -primary-preparatory -0.430*** 0.06 -0.401*** 0.05 -0.454*** 0.06 -0.403*** 0.04

Average degree -secondary degree -equivalent -0.649*** 0.05 -0.501*** 0.03 -0.720*** 0.05 -0.577*** 0.03

Above average degree but below university degree -0.794*** 0.10 -0.672*** 0.08 -1.138*** 0.12 -0.851*** 0.07

University degree -1.175*** 0.08 -0.928*** 0.06 -1.388*** 0.09 -1.095*** 0.06

Above university degree -6.133

-6.620

-1.730*** 0.42 -1.557*** 0.50

Employment status of the household head

Under employed -0.203 0.27

0.239 0.29

Employed normal number of days -0.529** 0.26 -0.187*** 0.06 -0.043 0.28 -0.134** 0.05

Over employed -0.374 0.27 -0.150** 0.06 0.011 0.28 -0.156*** 0.06

Employment status of the household head

Temporary 0.088 0.16 0.16 0.1 0.164 0.11 0.157** 0.08

Seasonal -0.027 0.3 0.025 0.17 0.104 0.25 0.292* 0.15

Occasional 0.510*** 0.07 0.410*** 0.04 0.385*** 0.06 0.305*** 0.04

Custom category

Manufacturing -0.227*** 0.07 -0.416*** 0.04 -0.360*** 0.07 -0.305*** 0.04

Construction -0.246*** 0.08 -0.182*** 0.05 -0.332*** 0.08 -0.198*** 0.04

Services -0.242*** 0.06 -0.307*** 0.03 -0.290*** 0.06 -0.271*** 0.03

Intercept -2.382*** 0.48 -1.519*** 0.22 -2.820*** 0.52 -2.397*** 0.25

Number of observations 16,145.00 22,192.00 15,729.00 21,336.00

Pseudo R2 0.28 0.22 0.29 0.24

Source: ibid.