equity in the basic education opportunities in egypt

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Equity In The Basic Education

Opportunities In Egypt

Eman Refaat Mahmoud

Rania AtefBy

2011

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Eman Refaat Mahmoud

Eman Refaat is a Researcher/Statistical Analyst at the Social Contract Center (SCC). The SCC aims at providing first class policy advice and policy options, coordinating and monitoring the implementation of the MDGs-based poverty action plan and promoting among various stakeholders a vision for a new social contract rooted in principles of democratic governance and modern concepts of citizenship. Eman Refaat worked on a number of research studies in development, poverty, MDGs, and issues related to monitoring and evaluation. She did the statistical analysis of many social studies and data analysis and, also participated in designing and measuring the youth wellbeing index of Egypt that was published in Egypt Human Development Report of 2010.

Eman Refaat has a B.Sc. in Statistics from the Faculty of Economics and Political Science in Cairo University.

Rania Atef

Rania Atef is a Researcher/Statistical Analyst at the Egypt National Child Rights Observatory which aims at promoting child rights and evidence-based legislation, public policies, programmes and budget allocation. She is responsible for conducting research and analyzing available data on children. She lead research on developing a set of national child rights indicators, conducted several expert meetings in different parts of Egypt to review and refine child poverty indicators used in Egypt. She is also a main author in the observatory ’s monthly research periodical: “Children in Egypt: Facts of Today… Vision for Tomorrow” which produced two issues so far, one on “Never Attending School” and the other on “Child Discipline at Home”.

She holds a M.Sc. in Social Research Methods and Statistics from London School of Economics, University of London in the United Kingdom. She earned her BSc. in 2002 in Statistics from the Faculty of Economics and Political Science in Cairo University.

About the Authors

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ACKNOWLEDGEMENT

We would like to thank Dr. Sahar El Sheinety for the technical support she provided to this study; it wouldn׳t have been possible to accomplish the goals of the study without her continuous guidance and valuable assistance.

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Convention on the Rights of the Child, Article 28Child Law Articles 54 and 59

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Education brings wide-ranging benefits to both individuals and societies. It is central for improving the quality of life since it is linked to improving several demographics, social and economic processes. Research has shown its positive effect in raising the economic status of families, improving life conditions, lowering infant mortality rate, and increasing educational attainment of the next generation, thereby raising the next generation›s chances for economic and social well-being. In addition, research worldwide shows that a better educated household is less likely to be poor. Thus, for poor households, educating children is crucial for breaking the circle of poverty that moves from one generation to the next. Thus, ensuring equality in educational opportunities for everyone should be a priority for all developing nations.

The Convention on the Rights of the Child states that state parties should make primary education compulsory and available free for all (1) . Furthermore, the Egyptian law acknowledges this right and includes appropriate articles that state that basic education (primary and preparatory) is compulsory and free for all (2) .

Out of school children may be classified into two groups, first group is the children who have never attended school and the second is the children who drop out of school before completing basic education.

This paper focuses on exploring the magnitude of the two phenomena and identifying their determinants for children in basic education aged (10 - 15). Moreover, it attempts to compare between the situation on the national level, rural level, and the level of the poorest 151 villages in Egypt where 84% of citizens are poor to test and highlight evident inequalities between the three levels (Social Contract Center, 2010). The results of this paper shed light on the magnitude of inequality of basic education opportunities in Egypt that is associated with having certain background characteristics in general and belonging to rural areas and poorer communities in particular. The purpose of such comparisons is to inform policy makers on how the situation in the poorest villages differ from the situation in the rural areas and the national level, and whether national action is needed or the magnitude of the problem is much bigger in certain communities that require targeted action.

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Deprivation of basic education can be divided into two forms, never attending school and dropping out of school before completing basic education. Those two types of problems are explored in separate sections.

As stated earlier, two separate sections discuss the problem of never attending school and that of dropping out before completing basic education. Each section compares proportions of children who never attended school or dropped out of school at the three levels; national, rural and poorest villages. Confidence intervals (95%) are calculated; if the intervals do not overlap, a significant difference could be assumed.

Another main component is to explore the determinants of each problem, and compare the magnitude of effect of several background characteristics across the three levels. As much as possible, the same variables will be explored on all three levels. Thus, the explanatory variables reflect background, socio-economic and individual characteristics. Those include type of place of residence, household head’s educational level, wealth index, number of children in school age in the household, household head employment type, sex of child, age group, and disability status.

For the poorest villages’ level, more variables are used since data were collected for them. These are household expenditure, Mother/non mother village, and availability of primary school in the village

A wealth index was constructed separately but using the same methodology (3) for each level; national, rural and poorest villages. Thus, the wealth quintiles in each level divide households of this level into 5 equal groups according to their ownership of certain goods and living conditions. Thus, in the poorest villages, the richest quintile represents the richest 20% in the poorest villages. The point is to measure the effect of relative wealth in each (National, Rural and 151 poorest villages) on never attending school or dropping out of school.

For examining the determinants of never attending school, a logistic regression model is used. Logistic regression is a technique for analyzing data where the response variable (in this case never attending school) is dichotomous. Logistic regression involves modeling differences between individuals in the probability that the response variable will occur, using multiple explanatory variables, in our case the background, socio-economic and individual characteristics.

The Principal component analysis and factor analysis were used for the calculation of the wealth index.3

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For the poorest villages’, the baseline survey conducted by the Social Contract Center, Information and Decision Support Center in (2009) was used. The sample design is a stratified probability sampling design (without clustering) with two levels of stratification of villages (mother villages and others not mother villages) with all hamlets and satellites included in the two strata. The sample design allows to all local units and villages to be selected in the sample. The design allows obtaining estimates for all required indicators and the differences (variability) in the estimation of these indicators with least possible standard error in all levels either governorate level, intervention villages, or the villages (mother villages, other not a mother village, hamlets and satellites). The baseline survey for the poorest villages was conducted in (2009) where a total of 6,993 households were successfully interviewed in 151 poorest villages, the number of individuals aged (10 - 15 years old) was 5373 individuals. Education module in addition to some of the household and individual characteristics are utilized in this study.

For the national and rural levels, data from the Survey of Young People in Egypt (SYPE) conducted by the Population Council in (2010) was used. The SYPE sample is a nationally representative sample covering all governorates in Egypt, including the five Frontier Governorates. It is a stratified, multi-stages cluster sample. The survey covers a nationally representative sample of 15,029 young people aged 10 - 29 from 11,372 households. 5001 were between 10 and 15 years. The survey collected data in April/May 2009 on the five key areas of education, work, family formation, health and sexuality, and civic and political participation (Population Council, 2010). This paper mostly uses data from the household questionnaire for background characteristics and the education module.

A logistic model was not used for examining the determinants of dropping out of school, since it includes censored data, for we may not assume for example that those who are 11 and still attending school will not drop out before completing basic education. In general, survival analysis examines and models the time it takes for events to occur. In our case, the event is dropping out of school. Therefore, for examining the determinants of dropping out, a non-proportional hazards model is used to explore how the hazard of dropping out of education varies in response to explanatory covariates.

The age explanatory variable entered to the model to differentiate between primary and preparatory.

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In the poorest 151 villages, data of the baseline survey shows that primary schools are available in 98% of the villages, while the availability of preparatory education is somewhat less than primary education. Overall, preparatory schools are available in 82% of these villages (Social Contract Center 2010).

If certain background characteristics are proven to be associated with never attending school or dropping out of school, then we may assume that children living in communities with a high concentration of those background characteristics are more at risk of not completing basic education. In this section, differences in background characteristics in the three levels are compared to highlight any apparent difference before the association of background characteristics with never attending school and dropping out of school is examined.

The most striking difference is income poverty. Thus, whereas on the national level, 22% of all Egyptians are below the poverty line, 28% in rural areas are below poverty line, and 84% in the poorest 151 villages cannot satisfy their food and non-food basic needs (Social Contract Center 2010).

Table A in the appendix gives the distribution of several background characteristics on three different levels, national, rural and poorest villages. The table reveals a big difference in the distribution of household head’s educational level. Thus, approximately 60% of households heads in the poorest villages have no education compared to 27% on the national level and 35% on the rural level.

All other background characteristics do not differ extensively on the different levels. It must be noted however that disability seems to be underreported on all levels. Thus, in both rural and national levels, approximately 2% of the children aged 10 - 15 have a disability. The percent who has disability in the poorest villages is approximately 1%. Thus, there is clear underreporting of disability which may be due to unawareness or feeling ashamed of a disability.

Figure (1): Distribution of Education of Household Head (within National, Rural and Poorest Villages)

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Figure (2): 95% confidence interval for the Percent of children aged 10 - 15 who never attended school at the three levels; National, Rural and Poorest Villages

5.1 Never Attending School

In Egypt, children should officially enroll in school when they are six, however, they are entitled to enroll in school up until they are ten. Therefore, this paper explores the phenomenon of never attending school for children aged between 10 and 15. It investigates two main questions. The first is percent of children who have never attended school to assess the magnitude of the problem and the second is who is not attending school through exploring the profile of those who never attended school and determinants of never attending school. In answering both questions, the study compare the results on the national, rural and poorest villages levels.

5.1.1 Magnitude of the Problem

At the national level, 2.3% of children aged 10 - 15 have never attended school. At the rural level, this percent goes up to 3.3% as shown in Figure 2. However there is an overlap in the 95% confidence interval of national and rural proportion of never attending school and thus, we may not conclude that there is a difference between the national and rural level. On the other hand, the percent of children aged 10 to 15 who have never attended school in the poorest villages reaches 6.8%. It is significantly higher than both the national and rural levels. Thus, we conclude that the problem of never attending school is significantly more serious in the poorest villages than in the national level and even the rural level and could be attributed to poverty rather than living in rural context.

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5.1.2 Factors and Determinants

This section explores the relationship between some background characteristics and never attending school. It considers individual characteristics such as sex, disability status, and age group; socioeconomic characteristics such as wealth index, household head’s educational level, household head’s employment type, and number of school-aged children in the household; and community characteristics such as type of place of residence.

Furthermore, for the poorest villages’ level, more variables will be explored since data were collected for them. Those are household expenditure, mother/non mother village, and availability of primary school in the village.

In the first part, the association between each variable and never attending school will be examined separately through univariate analysis and compared at the three levels. The second part will fit a logistic regression model for each level using all variables to test the significance of each variable and the magnitude of its effect while controlling for the other variables.

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Sex is evidently related to never attending school. Thus, as Figure (3) shows, the percent of girls who never attend school is always higher than boys at the three levels. Females in the poorest villages have the highest percent of never attending school with approximately 1 in every 10 girls there never attending school, which almost double the rural level.

Although disability is usually under-reported, and thus the number of children with disability is small in our data, but still analysis reveals that disability is strongly associated with never attending school. This is reflected by the difference between the percent of children with disability who never attend school and that of children with no disability. Moreover, percent of children with disability in the poorest villages who never attend school is significantly higher than those in rural areas and on the national level. Thus, 54% of children with disabilities have never attended school at the poorest villages level, this percentage is 23% at rural areas level and 18% at the national level.

Figure (3): Distribution of children never attented school by gender

18.3% 23.3%

53.6%

2.0% 2.9% 6.3%0%

20%

40%

60%

80%

100%

National level Rural areas Poorest 151 villages

Have disablility No disability

Figure (4): Percentage of children never attended school by Disability

Figure (4): Percentage of children never attended school by Disability

Considering the age categories to discriminate between those in primary school age (10 - 12) and those in preparatory school age (13 - 15) among our targeted group, the data shows that at the national, rural areas, and 151 poorest villages level the percentage of children 10 - 12 years old who have never attended school is smaller than the one of 13 - 15 years old, These provide clear evidence that education deprivation has been declined on all levels in general. It must be noted that the rate of declination is higher on the national and rural levels compared to that of the poorest villages.

A. Factors Associated with Never Attending School

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Table (1): Percentage of Children who never attended school by age group.

Table (2): Distribution of children who never attended school at the different levels by wealth index quintiles.

Since the wealth index was created separately for each level, it is a measure of relative wealth at this particular level. Thus, the richest quintile of the poorest villages is not equivalent to the richest quintile on the national level nor the rural one, but only reflects relative wealth compared to others in the poorest villages. At all levels, the wealthier the households, the lower the percent of children who never been to school. The situation is worst in the poorest villages followed by rural areas. Thus, the percent within each quintile that never attended school is always highest in the poorest villages compared to the national and rural levels. At the national level, 6.3% of children in the poorest (lowest) quintile of the wealth index have never attended school, this percentage is 8.5% at rural areas while in the poorest villages level the percentage is 13.4%.

The expenditure quintiles for the households in the poorest 151 villages show that, among the first quintile there are 13.4% of children who have never been to school and 10% among the third quintiles, while the third, fourth and highest have lower percentages respectively (6.7%, 3.6% and 1.5%).

AgeNational level Rural areas Poorest 151 villages

10 - 12 5.5 2.2 5.5

13 - 15 3.1 4.5 8.2

Percentage Never Attended School

Wealth index quintiles National level Rural areas Poorest 151 villages

Lowest 6.25 8.45 13.4

Second 1.94 3.59 10.0

Middle 0.76 1.72 6.7

Fourth 0.7 0.58 3.6

Highest 0.13 0.42 1.5


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Table (3): Distribution of never attended school at different levels of education of household head

Children aged 10 - 15 years who are living in households with an uneducated household head have the highest rates of never attending school compared to those living in households whose household heads are educated. The pattern seems to be similar across all three levels. Never attending school seems to be prevailing mostly amongst children whose household heads had no education. There is a significant drop in the percent of children who never attended school if the household head had at least some primary education than if she/he had no education. At the national level, percent of children whose household head had no education and who have never attended school is 6%, 7.3% at the rural areas level, while it is 10% in the poorest villages.

Also children living in households where the household head has a non permanent job have higher percentages of never attending school than those who have household heads with permanent jobs. 4.0% of children in households where household head has a non permanent job have never attended school at the national level versus 1.4% for those with permanent job. At the rural areas this percentage is 5.3% versus 2% and moves higher to be 10% versus 4% at the poorest 151 villages.

One of the considered factors is the number of children in basic education aged (7 - 15 years old) inside the household. The percentage of children who never attended school among households with 5 - 7 children in basic education age is the highest for all groups, national, rural, and poorest 151 villages, 10%, 9%, and 17% respectively.

Education of household head

National level Rural areas Poorest 151 villages

Uneducated 6.01 7.26 10.0

Primary 1.38 1.4 4.7

Preparatory 0.98 1.26 1.8

Secondary, technical or diploma

0. 49 0.63 0.4

University \ higher 1.07 2.71 1.8


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1.9% 2.9% 6.1%3.2% 3.8% 6.8%10.1% 8.9%16.9%

0%

20%

40%

60%

80%

100%

National level Rural areas Poorest 151 villages1-‐2 3-‐4 5-‐7

Figure (5): Distribution of children who never Attended school by number of children in the household in basic education age

The percent of children 10 - 15 years old who have never attended school in the poorest 151 villages varies by governorate. This percent ranges from as high as 11% in Menia to a low level of 2% in Sharkia. In general, data of the poorest villages reveal that villages in Upper Egypt tend to have relatively higher never attending school percentages than the two villages in lower Egypt.

This is also reflected in national and rural level data where the highest percentage of never attending school is among rural frontier (10.6%) and rural upper Egypt (5.2%) while the lowest percentage is among children living in urban lower Egypt (0.62%).

Figure (6): Percent of children who never attended school by governorates in the poorest villages

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Table (4): Percentage of children who never attended school by region (national and rural levels)

For the type of villages in the poorest 151 villages as it is a mother or non mother villages, the percentage is almost the same in both 7% in mother villages and 6.8% in non mother one. Among those who live in the hamlets of the village 7.9% of children never attended school versus 6.1% attended school among those who live inside the village.

Region National level Rural areas

Urban Governorates 0.85 -

Urban Lower Egypt 0.62 -

Rural Lower Egypt 1.53 1.53

Urban Upper Egypt 1.34 -

Rural Upper Egypt 5.16 5.16

Urban Frontier 2.3 -

Rural Frontier 10.59 10.59


B. Determinants of Never Attending School

In many statistical applications and when using the method of multiple regression, the response variable may be binary (which takes only two values), it reflects the presence / absence of the phenomenon or event, and therefore multiple regression method can›t be applied in this case. To find out the relationship between the response and explanatory variables or the most important determinants that are associated with the binary response variable logistic Regression is used.

In this section, the study apply a logistic regression model to predict the probability of occurrence of the event, our response variable “never attending school”. It also allows for studying the association between each of the explanatory variables separately on the response variable with holding all other variables constant. Interpretation of the model parameters depends on the odds ratio for the occurrance of the event under study. Odds of an event is the ratio between the probability of its occurrence and the probability that it does not occur. For example, if the probability of occurrence of an event is 0.8, the probability of failure is 0.2, and accordingly the odds of the event is 4. Thus, the greater the odds the higher the probability of event occurrence, and vice versa.

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The response variable in the model is “never attending school” and examined determinants include explanatory variables that reflect background (governorate, region, mother/ non mother villages, availability of primary school…etc), socio economic (wealth index, expenditure quintiles, household head’s educational level, household head’s job type and number of children aged 7 - 15 inside the household) , and individual characteristics (gender, disability, and age) mentioned above. Table (B) in the annex gives the results of the logistic regression. For each variable, odds ratios are presented in the table, which indicate the relationship between the variable and the odds of never going to school when controlling for all other variables. An odds ratio greater than one indicates a positive relationship between the variable and exposure to never going to school, while an odds ratio of less than one indicates a negative relationship between the variable and never going to school.

Results show that, the overall models at the three levels (national, rural, and poorest 151 villages) are significant. While holding all other variables constant, the relation between never attending school and wealth index shows that at the national level the odds of a child never going to school is reduced by 97% if the child׳s household belongs to the richest wealth quintile rather than those who live in the poorest wealth quintile, at the rural areas this percent is 92%, and at the poorest villages level the percent tends to be 74%. As we move down to the fourth, middle, second quintile the reduction percentage go down at all levels (national, Rural, 151 poorest villages) as shown in Table 5, but at the poorest 151 villages there is no significant difference between children living in lowest wealth quintile and those at the second wealth quintile.

The results imply that wealth is a very strong determinant of never attending school, however, since wealth variation is more extreme in the national level, followed by the rural level and then the poorest 151 villages.

Table (5): Association of wealth Quintile with change in odds of never attending school

Wealth Quintile National level Rural areas Poorest 151 villages

Second -62%* -56%* -14%

Middle -67%* -74%* -36%*

Fourth -68%* -88%* -57%*

Highest -97%* -92%* -74%*

Percentage compared to lowest quintile

Reference category: First Quintile (Poorest)(*) Estimate significant at the 0.05 level.

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When it comes to the education level of household head, there is no significant difference between those in university or higher education level and the households with no education at the national level and the rural areas. While at the national level the odds of a child never going to school is reduced by 80% if the education level of household head is secondary, technical or diploma rather than a household head with no education. At the rural areas the percentage is almost the same (81%), but at the poorest 151 villages level this percentage is very high (95%).

In the poorest 151 villages and when the job of the household head is permanent, the odds of a child never going to school is reduced by 36% compared to children where the household head›s job is not permanent. There is no significance difference between children never going to school among the job types of household head at the national and rural areas.

Number of children in the basic education age (7 - 15) inside the household have relation with the never going to school. When this number ranged from 5 to 7 children, the odds of never going to school increased by 125% than those households with (1 -2 ) children. At the poorest 151 villages, there is no significance difference between households with 1 - 2 children and households with 3 - 4 children. In addition to that, at the national and rural areas there is no effect for the number of children in basic education age (7 - 15) inside the household on never going to school.

Males in general have better chance in going to school than females. Results show that the odds of females never going to school increases by 272% at the national level, 419% at the rural areas, and 193% at the poorest villages level compared to males.

Children in primary education aged (10 - 12) are less likely to never attend school by

Table (6): Association of Household Head Education Level with change in odds of never attending school


Second -72%* -78%* -49%*

Middle -79%* -82%* -71%*

Fourth -80%* -81%* -95%*

Highest -13% +22% -66%*

Reference category: No education(*) Estimate significant at the 0.05 level.

Percentage

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47% at the national level, 49% at the rural areas level and 32% at the poorest villages level than children in preparatory education age (13 - 15), which affects the increase in enrollment rates across these two groups.

Having a disability raises the chances of never going to school significantly, children who have disability ’s odds of never attending school is 18 times more than children without disabilities at the national level, this value is 25 and 26 times for children in rural areas and poorest 151 villages respectively.

Children living in Menia and Assiut governorates do not differ from those living in Qena governorate in never going to school, while the odds of a child never going to school decreases by 37% for children living in Sohag than those living in Qena governorate. In addition to that, odds of children never going to school in Sharkia governorate and Behera governorate decrease by 72% and 61% respectively than children living in Qena governorate.

Living inside the village (4) in the poorest 151 village decrease the odds of a child never going to school by 29% than living in the hamlets of the village.

The regions have no significance difference in affecting never going to school at the national level and rural areas level. Also in the poorest 151 villages living in mother or non mother has no difference in affecting never going to school. Availability of primary school in the village has no significance difference in affecting never going to school. 5.2 Dropping Out of Basic Education

Dropout in this study refers to children in the age group 10 - 15 who have started school but dropped out before completing basic education (preparatory stage). It explores inequality in opportunities by comparing the situation at the national level, rural level, and the level of the 151 poorest villages in aspects such as magnitude of the problem, school years with significantly high dropout rates, factors of dropout, determinants of dropout and reasons for dropout. Thus, the next four sections discuss four main questions regarding the problem of dropping out of basic education: How many (percent), when, who, and why? with the main goal of highlighting similarities and capturing differences in the answers for the above questions on the national level, rural level and level of the poorest 151 villages.

This section explores whether there is a significant difference between magnitude of dropout on the national level, rural level and level of the poorest villages. It compares

Each village have hamlets, households that are living inside the village have more access to different facilities than those living in hamlets.

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the proportion of children aged 10 - 15 who have already dropped out by the time the survey was conducted. It must be noted here that looking at the proportion of children who dropped out at the time of the survey ignores the fact that this is censored data and that the proportion measure does not take into consideration children who have not yet dropped out but may do so before they complete basic education. However, the point here is just to examine the difference in the phenomenon between the three levels at a certain point in time. To do so, the study use a 95% confidence interval for each level to be able to conclude if there is a significant difference between the three levels. As Figure 7 below shows, proportion of children who dropped out is highest at the poorest villages followed by the rural level and then the national level which has the lowest dropout proportion. Nevertheless, the figure also shows that the rural confidence interval overlaps with both the national and the poorest villages confidence interval. Thus, it may not be assumed that there is a difference between the rural and the national level nor the rural and the poorest villages level. On the other hand, there is evidence that the proportion of children who dropped out at the poorest villages is significantly higher than the national level. Since their confidence intervals do not overlap, this implies that there is a difference between them. In the poorest villages, 6.87% of children have dropped out by the time the survey was conducted, compared to 4.87% at the national level.

5.2.2 School Years of Significant Dropout Rates

An important thing to explore when studying dropping out of school, is when children are most at risk of dropping out. To study this, the Kaplan-Meier survivor function was used. The Kaplan-Meier estimator is a nonparametric estimate of the probability of survival past time t (Cleves, Gould and Gutierrez, 2004). Figure (8) shows the survivor function over the years of education completed successfully. As the figure shows, the pattern is very similar across all levels, national, rural and poorest villages.

Figure (7): 95% confidence interval for Proportion of children aged 10 - 15 who dropped out at the three levels; National, Rural and Poorest Villages

5.2.1 MAGNITUDE OF THE PROBLEM

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5.2.3 Reasons for Dropping Out

In all three models, there were three main reasons given for dropping out of school, the most popular of which was that the child did not want to continue education. The other two reasons were that education cost is too high or that the student is not doing well at school. It is worth noting that whereas, financial hardship was expected to affect dropout more in the poorest

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29 9

8 4

14

40

6

1924

310

79 6

11

44

7

2116

6

49

23

0

10

20

30

40

50

60

National Rural Poorest Villages

Figure (9): Reasons for dropping out of school in different levels (poorest, rural and national)

The slope is steepest between years 5 and 6, implying the highest dropout happens after completing 5 years. Furthermore, the slope is steeper in poorest villages than in national and rural levels, again implying that the dropout is particularly high between years 5 and 6 especially in the poorest villages or in other words between moving from primary stage to preparatory stage. The dropout of primary is the highest in the 151 villages followed by rural then national level.

Figure (8): Kaplan Meier Survivor Estimate

villages compared to the national level, the reasons reported suggest otherwise. Thus, education cost was a less popular reason for dropout in the poorest villages than it was on the national level and rural level. A more popular reason was that the child did not want to continue education or that he was not doing well in school or if he/she was working at the same time to earn money and then no time to study.

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On the national and rural level, there appears to be a different pattern for reasons given by boys or girls. Thus, not wanting to continue education and not doing well at school were reasons that were more popular amongst boys than girls on the national and rural levels. On the other hand, a financial reason was more popular amongst girls than boys. On the contrary, in the poorest villages, there did not seem to be reasons that are more popular amongst a certain sex.

5.2.4 Determinants of Dropping Out

This section attempts to compare significant determinants of dropout on three different levels, national, rural and poorest 151 villages. First, it highlights the significant determinants on each level, then compares the effect of variables at each level. To explore the determinants of dropout, a non-proportional hazard model was applied to each data set. In general, survival analysis examines and models the time it takes for events to occur. In our case, the event is dropping out of school.

Table (C) in the appendix gives the results of the hazard model. For each variable, hazard ratios are presented in the table, which indicate the relationship between the variable and the risk of dropping out of school before completing basic education when controlling for all other variables. A hazard ratio greater than one indicates a positive relationship between the variable and risk of dropping out, while a hazard ratio of less than one indicates a negative relationship between the variable and dropping out. The second column of P>Z presents the significance of the variable, if its value is less than or equal to 0.05, the variable is considered significant at the 95% confidence level (5) .

Figure (10): Reasons for Dropping Out of School by Gender and level (poorest, rural, and national)

Including survey design with time varying covariates was not possible and this may have affected the significance of some variables. Therefore, any variables that were not significant at least at the 95% level were considered insignificant

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The last column presents the 95% confidence interval for the hazard ratio. Thus, we may say that we are 95% confident that the value of the hazard ratio is between the lowest and highest values of this column. This column will help conclude if there is a difference in the effect of variables at each level. Thus, if the confidence interval of the three levels overlap, then we may not conclude that there is a significant difference.

Some characteristics were significant determinants in all three cases. Thus, residence, wealth, level of education of household head, and age group were all significant variables that relate to the risk of dropping out on the three levels. After controlling for all other variables, both sex and employment status of household head did not show a significant effect on any of the three models at the 95% level.

In both the national and rural levels, number of children in the household in school age are also associated with the risk of dropping out. Disability status was a significant determinant only on the national level. Finally, in the poorest villages, other variables showed a significant relation with the risk of dropping out, those are whether this village is a mother village, availability of a primary school, and expenditure. The latter are variables that were not available on the national and rural level and thus, it may not be assumed that they necessarily differ from the national and rural level. However, we may only conclude that they have a significant relation with dropping out in case of the poorest villages.

Educational level of household heads was highly associated with the risk of dropping out in the three levels. There is a negative relationship between dropping out and education of household head. Thus, as the household head educational level increases, the risk of dropping out of children decreases. Moreover, the pattern was more or less the same in the three models, and the confidence intervals of the hazard ratios of the three models overlapped implying that we may not conclude a difference in the effect of household education in the three levels. Thus, particularly a child whose household head had secondary education faced a risk of dropping out that was significantly less than a child whose household head had no education by similar values in the three cases, by 76% in national level, 77% in rural and 82% in the poorest villages. Table (7): Relationship between Household Head Education Level and Risk of Dropping out of Basic Education

Household Head Education Level National level Rural areas Poorest 151 villages

Primary -8% -33% -42%*

Preparatory -49%* -67%* -32%

Secondary -76%* -77%* -82%*

University -76%* -100%* -92%*

Relation to Risk of Dropping out

Reference category: No Education, (*) Estimate significant at the 0.05 level.TVC refers to time varying covariates’ effect over time

22

Long-term wealth reflected here by the wealth index shows more variation in its effect in the national and rural levels than it does on the level of the poorest villages. Thus, whereas on the national level, the risks of dropping out of children is significantly reduced as the child moves from the poorest quintile to any other quintile, on the rural level, the relationship starts being significant as the child moves from the poorest quintile to the middle quintile, in the poorest villages, the relationship is only significant as the child moves from the poorest to the richest quintile. Moreover, the effect of moving from the poorest to the richest quintile in the poorest villages is almost equal to the effect of moving from the poorest to the second quintile in the national level (approximately 49% decrease in risk of dropping out in both cases). This result is realistic since in the poorest villages difference between households in terms of assets and shelter conditions, are not extreme, unlike in the national level. Thus, Table 8 below shows that wealth has a great effect on the national level, and that a child in the richest quintile faces a risk 94% less than a child in the poorest quintile. This effect is less in rural areas, where differences in wealth are less extreme and thus, the child in the richest quintile faces a risk 74% less than a child in the poorest quintile. Finally, in the poorest villages, a child in the richest quintile faces a risk 49% less than a child in the poorest quintile.

It is worth noting here that in the poorest villages model, there was a significant difference between the effect of expenditure level on dropping out only for children in households with the highest expenditure compared to those with the lowest expenditure (see appendix for hazard model output). Thus, children in households with the highest expenditure face a risk of dropping out 68% higher than children in households with the lowest expenditure. This could imply that families in the poorest villages with the highest expenditure choose to take their children out of school whenever they experience shocks in income because of their relatively high expenditure.

Table (8): Relationship between Household Wealth Quintile and Risk of Dropping out of Basic Education


Second -49%* -23% -15%

Middle -49%* -41%* +5%

Fourth -71%* -37% -22%

Highest -948% -74%* -49%*

Relationship with Risk of Dropping out

Reference category: First Quintile(*) Estimate significant at the 0.05 level.

Children in the primary stage age group face a risk less than children in the preparatory age group in all three levels, national, rural and poorest 151 villages. Although the relationship seems to be greater in the rural followed by the national and then the poorest 151 villages, their hazard ratios confidence intervals all overlap and therefore we may not conclude that the magnitude of their effect is different.

23

Residence was amongst the significant determinants of dropout. On the national level, differences amongst regions were explored, and Rural Upper Egypt was the reference category. In the rural areas, Upper Egypt was also the reference category. In the poorest villages, Qena was the reference category. In the poorest villages, there was no significant difference between Qena and Assuit nor Qena and Menia. The biggest difference was between Qena and Behera. Thus, children living in Behera have a risk of dropping out of basic education that is 11.7 times higher than those living in Qena, but this risk decreases by 30% with each year of enrolment. Thus they are at a risk that is 1.4 times higher at the end of primary education. The risk of dropping out of basic education was also 2.24 times higher in Sharkia and 1.64 times higher in Sohag. Those results are similar to the results in the rural level, where children in lower Egypt have a risk of dropping out of basic education that is 3.52 times more than those living in Upper Egypt. Similarly in the national level, children living in rural lower Egypt have a risk of dropping out that is 3 times higher than those living in Rural Upper Egypt. Moreover, those in urban governorates have a risk of dropping out that is 8.34 times more than those living in rural upper Egypt but this risk decreases by 18% with each year of enrolment such that they are at a risk that is 2.5 times higher at the end of the primary education stage.

Age Group National level Rural areas Poorest 151 villages

Residence National level Rural

Residence (governorate)Poorest 151 villages

10 - 12 -43%* -45%* -33%*

Urban Governorates 8.34*Tvc: -18%*

NA Sharkia 2.24*

Urban Lower Egypt 1.29 NA Behera 11.73*

Rural Lower Egypt 3.02* 3.52* Menia Tvc: -30%*

Urban Upper Egypt 1.50 NA Assiut 1.22

Urban Frontier 1.32 NA Sohag 1.43

Rural Frontier 1.65 1.70 1.64*



Reference category: 13 - 15 years(*) Estimate significant at the 0.05 level.

Table (10): Relationship between residence and Risk of Dropping out of Basic Education

Table (9): Relationship between age group and Risk of Dropping out of Basic Education

Reference category for poorest villages: QenaReference category for national level and rural level: Rural Upper Egypt(*) Estimate significant at the 0.05 level.TVC refers to Time varying covariates’ effect over time

24

The models of the poorest villages had variables that were not available in the other two models due to unavailability in their data. Both were significant. Thus, children in mother villages faced a risk of dropping out that was 3.63 times higher than those in non-mother villages. However, this risk decreased by 25% each year. Moreover, children with a primary school available at their village faced a risk of dropping out that was 51% less than children with no primary school in their village.

Number of children in school age that lived in the household had no significant effect in the 151 poorest villages. However, in both the national level and the rural level, a child who lived in a household with 3 - 4 children in school age, faced a risk that is 1.29 more on the national level and 2.8 more on the rural level than a child who lived in a household with 1 - 2 children in school age.

Finally, disability status had a significant effect only on the national level. Thus, a child who had a disability faced a risk of dropping out that is approximately 2 times higher than a child who has no disability.

Table (11): Association of residence with Risk of Dropping out of Basic Education

Other Background Characteristics National level Rural areas Poorest 151 villages

Mother Village NA NA

Primary School is Available

NA NA -32%

Number of Children in School age 3 or 4

1.296* 2.80* -82%*

Disability 2.0193* 1.87 -92%*

Association with Risk of Dropping out

(*) Estimate significant at the 0.05 level.TVC refers to Time varying covariates’ effect over time

3.63*

TVC = 25%*

25

Inequality in basic education opportunities is mainly due to certain differences in background characteristics of children. Thus, a high concentration of certain characteristics that are associated with not attending/completing basic education in certain communities are likely to relate to high rates of never attending school and dropping out of school at those communities. There is evidence that never attending school rates and dropout rates in the poorest villages are significantly higher than the national rate. Moreover, there is evidence that never attending school in particular is significantly higher in the poorest villages compared to even the rural level.

Determinants of Never Attending School and dropping out of school show both communalities and differences. Furthermore, some determinants have an effect on all three levels, national, rural and poorest 151 villages while others have a significant effect only on one or two of those levels.

Wealth status, level of education of household head and age group are common significant covariates of both never attending school and Dropping out of school. The latter reflects children in preparatory stage compared to those in the primary stage. Moreover, the three are significant in all three cases; national level, rural level and level of the poorest villages. Age group shows higher odds of not attending and risks of dropping out for children in preparatory stage compared to those in the primary stage. Wealth seems to have a stronger effect in the national level than at the poorest villages level, this is justified since the variation of wealth on the national level is much more extreme than that at the poorest villages level. The effect of wealth also appears to be stronger on never attending school than on dropping out of school. On the other hand, household head education level and age group seem to have similar effects both on the national and poorest villages level. Thus, the higher the education of the household head the less likely the child is to never attend school and the less likely the risk of the child to dropout of school.

Sex and Disability status are highly significant covariates in the model of never attending school on all three levels. Females are more likely to never attend school than males. And children with disability are much more likely to never attend school than those with no disability. On the contrary, sex was an insignificant determinant of dropping out of school in all three levels. Disability was also insignificant in the rural and poorest villages dropout model. It was marginally significant at the national level. Thus, children with a disability were more likely to dropout than those with no disability at the national level.

Number of school aged children in the household affected the odds of never attending school only on the level of the poorest 151 villages. Thus, children living in households with 5 - 7 children in school age were less likely to have attended school than those living in households with only 1 - 2 children in school age.

26

In the poorest villages, living in Qena increased the odds of never attending school compared to living in villages of Lower Egypt. On the other hand, living in Qena decreased the risk of dropping out of school compared to villages of Lower Egypt. Similarly, on the national level, living in Upper Egypt also reduced the risk of dropping out compared to Lower Egypt and Urban Governorates.

Thus, whereas never attending school is more common in Upper Egypt and Frontier Governorates, dropping out of school seems to be more common in Lower Egypt and Urban Governorates. The most popular reasons for dropping out of school before completing basic education were the same on all three levels. The most popular was that the child did not want to continue education. The other two reasons were that the child was not doing well at school and financial cost of education. This raises questions on the effect of the quality of the educational experience and mechanisms of retaining children in schools.

To conclude, being poor and belonging to a household where the household head has no education were two background characteristics that had a strong association with both never attending school and dropping out of school. Both characteristics were significantly more prevailing in the poorest villages and thus, there was a significant difference in the percent who never attended school and those who dropped out of school before completing basic education. Thus, children living in communities where both illiteracy and poverty are high are still more vulnerable and more at risk of not getting the opportunity to receive and complete basic education.

27

Two main factors seem to be strongly associated with never attending school and dropping out of school, wealth status of households and household head educational level. Thus, there is a necessity for providing appropriate conditional financial support for the poorest families to be able to send their children to school after taking into consuderation the following two points:

Ensuring a better quality of educational services in the schools - in particular the schools located in the poorest areas in away that will encourge the children enrolled to continue in schools.

Operationalizing practically the principle of ״ Free Education ״ by waiving school fees completely and providing a free school uniform especially for girl students.

Moreover, there are needs to more targeted awareness campaigns for uneducated parents on the importance of education of children and the long-term benefits of education. A first step would be to carry out in-depth research on the uneducated parents’ perspectives of education.

Furthermore, schools needs to be accessible to and accommodating the needs of children with all forms of disabilities while promoting inclusion of all children with disabilities in regular schools. In parallel, awareness campaigns have to be undertaken to increase the awareness of parents on the rights of children with disabilities and particularly their right to education.

It must also be noted, that reasons reported for dropping out of school imply factors that might be related to school quality or the effectiveness of schools in retaining students. Thus, their needs to be in-depth research on reasons why some children do not want to continue their education. Moreover, there needs to be research on the existence of or evaluation of existing mechanisms of retaining students and avoiding dropping out. Similarly, there needs to be an evaluation of the current system of providing extra assistance to children who are not doing well at school to avoid their dropping out.

28

REFERENCES

1. Mario Cleves, William W. Gould, Roberto G. Gutierrez, and Yulia Marchenko (2008), An Introduction to Survival Analysis Using Stata, 2nd Edition, ISBN.

2. Population Council (2010), Survey of Young People in Egypt – Final report, Population Council, Inc.

3. Social Contract Center (2010), Poor by Design, Vulnerable at Best ״Findings of the Baseline Assessment for Phase – I of the Egyptian Government Initiative to Develop the Poorest 1000 villages״, Social Contract Center.

4. UNESCO (2005), Children Out Of School: Measuring Exclusion From Primary Education, UNESCO Institute for Statistics.

29

Table (A): Distribution of Background Characteristics in the three levels: National, Rural and Poorest Villages

Background Characteristics

% National level % Rural Areas % Poorest 151 Villages

Governorate

Region

Sharkia 16.90%

Behera 7.80%

Menia 24.20%

Assiut 14.70%

Sohag 16.30%

Qena 20.20%

Urban Governorates 19.09

Urban Lower Egypt 10.93

Rural Lower Egypt 31.25 52.92%

Urban Upper Egypt 9.77

Rural Upper Egypt 27.06 45.82%

Urban Frontier 11.4

Rural Frontier 0.75 1.26%

Have disability 1.91% 2.12% 1.10%

Don׳t have disability 98.09% 97.88% 98.90%

Male 50.03% 49.62% 52.30%

Female 49.97% 50.38% 47.70%

Disability

Gender

ANNEX 1

30

Living inside the village 60.50%

Living in hamlets of the village

39.50%

Mother / non mother villages

Mother village 29.90%

Non mother village 70.10%

Location of residence in village

Permanent 64.40% 60.75% 58.70%

Non-Permanent 35.60% 29.25% 41.30%

No education 27.76% 34.51% 59.80%

Primary 22.39% 24.55% 12.20%

Preparatory 9.38% 9.56% 3.30%

Secondary, Technical or Diploma

28.42% 24.76% 18.30%

University \ higher 12.05% 6.62% 6.50%

School is available 95.70%

School is not available 4.30%

1 - 2 69.10% 65% 49.10%

3 - 4 30.03% 33.71% 47.40%

5 - 7 0.87% 1.29% 3.50%

Job type of household head

Education of household head

Availability of primary education

Number of children from 7 to 15 years old

10 - 12 51.29% 51.23% 50.30%

13 - 15 48.71% 48.77% 49.70%

Age

31

Table (B): Logistic Model Results for never attending school on the three levels

Background Characteristics

% National level % Rural Areas % Poorest 151 Villages

Sharkia 0.278 00.148 , 0.521

Behera 0.386 0.0020.211 , 0.706

Menia 1.132 0.5290.770 , 1.664

Assiut 0.994 0.9760.654 , 1.508

Sohag 0.631 0.0450.403 , 0.989

Mother village ( mother) 1.273 0.0950.958 , 1.691

Location inside village 0.712 0.0170.538 , 0.942

Primary school is available 1.105 0.7360.618 , 1.977

Urban Lower Egypt 0.764 0.725.16, 3.44

Rural Lower Egypt 0.692 0.453.263, 1.81

Urban Upper Egypt 0.625 0.462.178, 2.19

Rural Upper Egypt 1.09 0.868.406, 2.90

4.812 0.26.779, 2.49

Urban Frontier 1.68 0.469.41,4 6.82

Rural Frontier 3.41 0.058.958, 12.20

1.39 0.0041.68, 13.758

Second 0.378 0.003.19, 0 .71

0.442 0.01.238, .819

0.861 0.3950.609 , 1.216

Middle 0.228 0.103, 0.505

0.262 0.002.112, .611

0.644 0.0250.438 , 0.945

Fourth 0.222 0.004.081, .608

0.122 0.001.034, .434

0.431 00.273 , 0.680

Highest 0.031 0.005.002, .351

0.083 0.003.016, .420

0.256 00.140 , 0.468

Odds Ratio

Odds Ratio

Odds Ratio

P>z P>z P>zCI CI CI

GovernorateRef Cat (Qena)

RegionRef Cat (Urban Governorates)

Wealth Index Quintile Ref Cat (Qena)Ref Cat (Lowest Poorest Quintile)

32

Second 0.62 0.0160.420 , 0.915

Middle 0.826 0.2880.580 , 1.175

Fourth 0.88 0.5740.564 , 1.374

Highest 0.824 0.4120.519 , 1.308

Primary 0.282 0.149, .534

0.221 0.101, .482

0.512 0.0020.333 , 0.789

Preparatory 0.213 0.007.069, .651

0.179 0.017.044, .734

0.287 0.0210.100 , 0.828


0.204 0.086, 0.483

0.189 0.004.061, .578

0.048 00.017 , 0.140

University \ higher 0.868 0.79.307, 2.45

1.22 0.715.417, 3.56

0.343 0.0270.133 , 0.884

Household Head Permanent job Ref Cat (Not Permanent)

0.75 0.187.488, 1.15

0.82 0.428.501, 1.34

0.643 0.0020.486 , 0.851

Expenditure QuintileRef Cat (Lowest)

Household Head Education LevelRef Cat (No Education)

Number of school aged Children Ref Cat (1 - 2)

3 to 4 1.18 0.50.73, 1.89

0.981 0.944.577, 1.66

1.07 0.6310.813 , 1.407

5 to 7 1.73 0.450.41, 7.28

1.09 0.902.244, 4.93

2.249 0.0021.350 , 3.747

Gender Ref Cat (Male) 3.729 02.31, 6.01

5.19 02.91, 9.24

2.927 02.226 , 3.849

Has Disability 17.91 07.90, 40.61

24.78 08.93, 68.73

25.884 09.902 , 67.662

Age Group (10 - 12) 0.538 0.008.341, .849

0.514 0.014.304, .871

0.678 0.0030.524 , 0.878

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Table (C): Non-Proportional Hazard Model Results for Dropping out of School on the three levels

-t״Poorest Villages״ ״Rural Areas״ ״National Level״

Main

Haz. Ratio

Haz. Ratio

Haz. Ratio

P>z P>z P>z95% CI

95% CI

95% CI

Sharkia 2.24 01.46 , 3.44

Behera 11.73 04.78 , 28.81

Menia 1.22 0.3890.78 , 1.92

Assiut 1.43 0.1170.91 , 2.24

Sohag 1.64 0.031.05 , 2.57

Mother village mother 3.63 0.0031.56 , 8.44

Location inside village 1.1 0.4760.85 , 1.41

Primary school is available 0.49 0.0010.32 , 0.75

Urban Governorates 8.34 03.185 21.887

Urban Lower Egypt 1.29 0.4470.666 2.513


3.02 0.011.302 7.007

Urban Upper Egypt 1.5 0.1360.879 2.583

Urban Frontier 1.32 0.4660.621 2.834

Rural Frontier 1.7 0.103.897, 3.24

1.65 0.1240.870 3.146

Second 0.85 0.4050.59 , 1.24

0.768 0.215.507, 1.16

0.606 0.0040.433 0.850

Middle 1.05 0.7810.73 , 1.51

0.507 0.007.308, .833

0.609 0.0110.416 0.895

Fourth 0.78 0.2180.53 , 1.16

0.633 0.093.371, 1.07

0.292 00.172 0.498

Highest 0.61 0.0370.39 , 0.97

0.256 0.001.114, .573

0.063 0 0.021

GovernorateRef Cat (Qena)

RegionRef Cat (Rural Upper Egypt)

Wealth Index Quintile Ref Cat (First Poorest Quintile)

34

Second 1.07 0.6940.75 , 1.54

Middle 0.91 0.6020.64 , 1.29

Fourth 1.29 0.2130.87 , 1.91

Highest 1.68 0.0071.15 , 2.46

Primary 0.58 0.0030.40 , 0.83

0.928 0.684.649, 1.32

0.919 0.5670.689 1.2269

Preparatory 0.68 0.2290.36 , 1.28

0.333 0.006.152, .729

0.407 0.0020.232 0.715


0.18 00.11 , 0.31

0.226 0.117, .436

0.242 00.149 0.396

University 0.08 00.02 , 0.31

0.00018 0.0473.87e-08 , .893

0.235 0.0030.0914 0.605

3 to 4 1.02 0.890.81 , 1.28

2.8 0.0291.10, 7.07

1.296 0.0550.995 1.689

5 to 7 1.38 0.2680.78 , 2.42

0.374 0.331.051, 2.71

1.06 0.9210.334 3.370

Female 1.06 0.6010.85 , 1.33

0.992 0.961.722, 1.36

0.522 0.0780.254 1.076

Has Disability 1.82 0.310.57 , 5.81

1.087 0.886.343, 3.44

2.0193 0.0530.992 4.111

Age Group (10 - 12) 0.67 0.0110.49 , 0.91

0.553 0.005.364, .840

0.571 0.0010.410 0.796

Behera 0.7 00.59 , 0.83

Mother village 0.75 0.0010.64 , 0.89

Female0.990 1.312

Urban Governorate 0.854 0.083.715, 1.02

0.6790 .996


0.746 1.024

Number of school aged Children (3 - 4)

3.22 0.0461.01, 10.2

Household Head Permanent job

0.89 0.3310.70 , 1.13

0.796 0.174.572, 1.10

0.916 0.5180.705 1.193

Expenditure QuintileRef Cat (First)

Household Head Education LevelRef Cat (No Education)

Number of school aged Children Ref Cat Ref Cat (1 - 2)

tvc

35

THE LOGISTIC REGRESSION EQUATION

Logistic regression analyzes binomially distributed data of the form

The model proposes for each trial i there is a set of explanatory variables that might inform the final probability. These explanatory variables can be thought of as being in a k-dimensional vector Xi and the model then takes the form

The logits, natural logs of the odds, of the unknown binomial probabilities are modeled as a linear function of the Xi.

Where β0 is called the «intercept» and β1, β2, β3, and so on, are called the «regression coefficients» of x1, x2, x3 respectively.

The interpretation of the βj parameter estimates is as the additive effect on the log of the odds for a unit change in the jth explanatory variable. In the case of a dichotomous explanatory variable, for instance gender, eβ is the estimate of the odds of having the outcome for, say, males compared with females

Each of the regression coefficients describes the size of the contribution of that risk factor. A positive regression coefficient means that the explanatory variable increases the probability of the outcome, while a negative regression coefficient means that the variable decreases

ANNEX2TECHNICAL ANNEXThis annex gives a summarized note about the main statistical methods used in the study namely; Logistic Regression Model, Cox Hazard Model and Factor Analysis for Wealth Index.

A. LOGISTIC REGRESSION MODEL

For examining the determinants of never attending school, a logistic regression model is used. Logistic Regression Model is a technique for analyzing data where the response variable (in this case never attending school) is dichotomous. Logistic Regression Model involves modeling differences between individuals in the probability that the response variable will occur, using multiple explanatory variables, in our case the background, socio-economic and individual characteristics.

Y i ~ B ( n i,p i), for i = 1,...m,

logit( p i)= = B 0 + B 1x 1, i + ... + B kx k, i.

36

B. NON PROPORTIONAL HAZARD MODEL

A logistic model was not used for examining the determinants of dropping out of school, since it includes censored data, for we may not assume that those who are still attending school will not drop out before completing basic education. In general, survival analysis examines and models the time it takes for events to occur. In our case, the event is dropping out of school. Therefore, for examining the determinants of dropping out, a non-proportional hazards model is used to explore how the hazard of dropping out of education varies in response to explanatory covariates.

To explore the determinants of dropout, a non-proportional hazard model was applied to each data set. In general, survival analysis examines and models the time it takes for events to occur. In our case, the event is dropping out of school. Table (C) in annex1 gives the results of the hazard model.

Like Life Tables and Kaplan-Meier survival analysis, Cox Regression is a method for modeling time-to-event data in the presence of censored cases. However, Cox Regression allows you to include predictor variables (covariates) in your models. Cox Regression will handle the censored cases correctly, and it will provide estimated coefficients for each of the covariates, allowing you to assess the impact of multiple covariates in the same model. You can also use Cox Regression to examine the effect of continuous covariates.

In a proportional hazards model, the unique effect of a unit increase in a covariate is multiplicative with respect to the hazard rate. Cox Proportional-Hazard Model is one of Survival analysis models.

the probability of that outcome; a large regression coefficient means that the risk factor strongly influences the probability of that outcome; while a near-zero regression coefficient means that that risk factor has little influence on the probability of that outcome.

In terms of probabilities, the equation above is translated into P= exp (β0 + β1*X1 + ... + βk*Xk) / (1+exp (β0 + β1*X1 + ... + βk*Xk).

Odds: This is the ratio of the probability of occurrence of an event to the probability of it not occurring. Odds can take on any value between 0 and infinity.

Reasons of Using Cox Proportional Hazard Model

The distribution of survival times is often skewed and not likely to be Normal distribution and it may not be possible to find a transformation.

The presence of censored observations*.

37

C. FACTOR ANALYSIS FOR CREATING WEALTH INDEX AT THE THREE LEVELS OF ANALYSIS (NATIONAL, URBAN-RURAL AND POOREST VILLAGES LEVEL)

Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. Factor analysis searches for such joint variations in response to unobserved latent variables. The observed variables are modeled as linear combinations of the potential factors, plus «error» terms. The information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset.

Data

Quantitative type of data corresponding to the time from a well-defined time origin until the occurrence of some particular event of interest or end-point.

Here is the basic model of Cox HZ: The time to event and the covariates are related through the following equation:

h(t) = [h 0(t)] exp(b 0+b 1x 1+b 2x 2+…+b px p)

h(t) : hazard rate at time t h 0(t) : baseline hazard at time t

h(t) = f(t)/S(t)

f(t): probability of event at time t S(t): survival probability at time t Hazard function h(t): Larger values of the hazard function indicate greater potential for the event to occur. Hazard Ratio (HR) : Represent the relative change in hazard rate for a unit increase in the regression. Though the Cox model is non-parametric to the extent that no assumptions are made about form of the baseline hazard, there are still a number of important issues which need be assessed before the model results can be safely applied. The key assumption in the Cox model is that of proportional hazards. The survival curves for two strata must have hazard functions that are proportional over time (i.e. constant relative hazard).

38

Type of factor analysis used in this study

Exploratory factor analysis (EFA) is used to uncover the underlying structure of a relatively large set of variables. The researcher›s a priori assumption is that any indicator may be associated with any factor. This is the most common form of factor analysis. There is no prior theory and one uses factor loadings to intuit the factor structure of the data.

Types of factoring used in this study

Principal component analysis (PCA): PCA seeks a linear combination of variables such that the maximum variance is extracted from the variables. It then removes this variance and seeks a second linear combination which explains the maximum proportion of the remaining variance, and so on. This is called the principal axis method and results in orthogonal (uncorrelated) factors.

Extraction Methods That Have Been Used In This Study

Principal-component factor (pcf): similar to principal component analysis where the communalities are assumed to be 1. It strictly does not correspond to a factorial analysis.

Maximum-likelihood factor (ml): Allows statistical test to determine the goodness of fit of the factor analysis in terms of reproducing of the correlation of the original indicators. Assumes multivariable normality.

For the Wealth index (Rustein and Johnston 2004), it is defined as a composite measure of the cumulative living standard of a household.

How the wealth index is measured?

Based on a set of assets and services assessed in the surveys

(e.g. Type of flooring, Refrigerator, Water supply, Type of vehicle, Sanitation facilities, Persons per sleeping room, Electricity, Ownership of agricultural land, Radio, Domestic servant, Television, Telephone)

Each household asset and service for which information is collected is assigned a weight or factor score generated through principal components analysis.

The first component of a PCA is interpreted as a continuous scale of relative wealth. The standardized scores are then used to create the break points that define wealth quintiles as: Lowest, Second, Middle, Fourth, and Highest.

The Wealth Index is used as a background characteristic when analyzing health status, or child rights.

39

In this study the wealth index was constructed using the following set of variables in the analysis to reach the final index:

Owning Refrigerator, Owning Color TV, Owning Black and white TV, Owning A large stove, Owning Apure and Jazz / Cooker small, Owning Electric fan, Owning Water Heater Bath, Owning Iron, Owning Radio / Radio recorder, Owning Washing Machine – normal, Owning Satellite, Owning Computer – Laptops, Owning Bicycle, Owning Landline, Owning Mobile phone, Owning Machinery and agricultural machinery, Owning Agricultural land, Owning Cattle / sheep, Owning Birds or rabbits, type of housing unit, kitchen availability, type of fuel used in cooking water source, sanitation type, Waste collection method and Average number of persons per room.

The same methodology was used for creating the wealth index at the all levels (National, Urban-Rural, and poorest villages’ level).

The lack of accurate, reliable and strategic information on the well-being of Egyptian children spurred the Social Contract Center and the Egypt National Child Rights Observatory (ENCRO) to create a partnership in 2010 aimed at producing evidence based research needed for the formulation of child centered and equity based policies. Both institutions vehemently believe that combining their know-how and knowledge in the area of equitable development, poverty and child centered research can add value to the enhancement of the well-being of children in Egypt.

In light of this partnership, a number of joint research projects of mutual interest that monitor and analyze the impact of poverty and disparities on the realization of children rights and well-being are undergoing.Framed in a development and right-based approach, this study aims to shed light on the magnitude of in-equality in basic education by comparing the determinants of children’s access to basic education at the national, rural, and at the level of the poorest 151 villages in Egypt. The study is the first of its kind to make such comparisons that will hopefully inspire stakeholders and decision-makers to take a more in-depth look at the possible range of policy responses and course of action that can help achieve more equitable educational outcomes.

The Egypt National Child Rights Observatory (ENCRO) is the national body mandated to carry out researches on children status and monitor the fulfillment of child rights and well – being in Egypt.

The Social Contract Center (SCC) is mandated to provide evidence-based policy advice to both government and development stakeholders, monitor& evaluate integrated development initiatives, and act as the catalyst for and a facilitator of a national dialogue with the aim to develop a consensus around a new social contract.

ENCRO and SCC Partners

equity in the basic education opportunities in egypt

Documents