metadata: authors: 0: author: Ersado,Lire
1: author: Gignoux,Jeremie D.
count: Egypt, Arab Republic of
docna: 0: docna: Egypt : inequality of opportunity in education
docty: Policy Research Working Paper
subtopic: Primary Education,Tertiary Education,Access & Equity in Basic Education,Educational Sciences,Educational Populations,Gender and Development,Education for Development (superceded),Education For All
teratopic: Education
abstracts: cdata!: The paper examines the levels and trends
            in access to education and educational outcomes across
            generations of Egyptian youth. Examination of three cohorts
            of individuals aged 21 to 24 (born between 1964 and 1967,
            1974 and 1977, and 1982 and 1985) shows that access to
            education has substantially improved during the last three
            decades. Completion rates increased by more than 60 percent
            at the preparatory level and 70 percent at the secondary
            level and the college completion rate more than doubled.
            However, significant inequities remain in access to
            education and educational outcomes. The fraction of never
            enrolled among the cohorts is still large, affecting more
            girls than boys, more rural than urban areas, and more
            children of parents with lower level of education and in
            elementary occupations, such as subsistence agriculture. The
            analysis of test-scores from the Trends in International
            Mathematics and Science Study (TIMSS) and national
            examinations shows that more than a quarter of learning
            outcome inequality is attributable to circumstances beyond
            the control of a student, such as parental education,
            socioeconomic background and place of birth. In Egypt,
            inequality of opportunity in learning outcomes emerges early
            and builds up progressively throughout the education levels.
            Access to higher education continues to remain significantly
            lower for children from rural areas and for those whose
            parents have a low level of education or are engaged in
            elementary occupations. Tracking into vocational and general
            secondary schools, which depends on a high-stakes national
            examination, and high and unequal levels of household
            expenditures in private tutoring substantially contribute to
            unequal learning outcomes.
geo_reg_key: 517202,119229,517191
repnme: repnme: Egypt : inequality of opportunity in education
display_title: Egypt : inequality of opportunity in education
listing_relative_url: /research/2014/08/19910408/egypt-inequality-opportunity-education
topicv3: Primary Education m1326139 480,Tertiary Education m1326349 482,Education m1327048 470,Education Access and Equity m1327050 2127,Gender m1327621 615
publishtoextweb_dt: 2014-08-01T00:00:00Z
historic_topic: Education
no_of_pages: 40
author_keys: 0: author_key: 457742
1: author_key: 930436
model_extractions: WPS6996 Policy Research Working Paper 6996 Egypt Inequality of Opportunity in Education _Lire Ersado Jeremie Gignoux_ Social Protection and Labor Global Practice Group August 2014
Policy Research Working Paper 6996

# **Abstract**

The paper examines the levels and trends in access to education and educational outcomes across generations of Egyptian youth. Examination of three cohorts of individuals aged 21 to 24 (born between 1964 and 1967, 1974 and 1977, and 1982 and 1985) shows that access to education has substantially improved during the last three decades. Completion rates increased by more than 60 percent at the preparatory level and 70 percent at the secondary level and the college completion rate more than doubled. However, significant inequities remain in access to education and educational outcomes. The fraction of never enrolled among the cohorts is still large, affecting more girls than boys, more rural than urban areas, and more children of parents with lower level of education and in elementary occupations, such as subsistence agriculture. The analysis of test-scores from the Trends in International Mathematics and Science Study (TIMSS) and national examinations shows that more than a quarter of learning outcome inequality is attributable to circumstances beyond the control of a student, such as parental education, socioeconomic background and place of birth. In Egypt, inequality of opportunity in learning outcomes emerges early and builds up progressively throughout the education levels. Access to higher education continues to remain significantly lower for children from rural areas and for those whose parents have a low level of education or are engaged in elementary occupations. Tracking into vocational and general secondary schools, which depends on a high-stakes national examination, and high and unequal levels of household expenditures in private tutoring substantially contribute to unequal learning outcomes.

This paper is a product of the Social Protection and Labor Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lersado@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Egypt: Inequality of Opportunity in Education

Lire Ersado and Jeremie Gignoux

**Keywords:** _educational inequality, educational achievement, inequality of opportunity, tracking, private tutoring, Egypt._

**JEL codes:** _D63, I24, I28._
# **1. Introduction and Motivation**

The 2011 uprisings in Egypt and the wider Arab world have been at least in part fueled by lack of economic opportunities and apparent social injustice. Since then, the distribution of economic opportunities and how they are shaped by public policies have taken center stage. An effective delivery of education services and expanding opportunities in the labor market<sup>1</sup> may go a long way in addressing the demands of the citizens, particularly the youth, which epitomized the uprisings: bread, dignity, opportunity and social justice. While Egyptians have benefited from improved access to education in the last several decades, education system is facing several challenges and some of these may have contributed to the discontent of the youth. First, the expansion of schooling has been accompanied by limited improvements in labor market outcomes (e.g. Pritchett 1999). In addition to weak job creation and rigidities in the labor markets, the disappointing labor outcomes may have their origin in poor quality education and mismatch in the skills acquired and that demanded in the labor market.

Second, there is a widespread perception of injustice within the Egyptian society that the chances to acquire good education differ vastly among socioeconomic and geographic groups (e.g., Binzel, 2011). Several features of the Egyptian education system may have contributed to the perceived inequities. The distribution of public resources tends to be skewed towards higher education and youth from disadvantaged backgrounds have very little chance of availing themselves to benefit from such public outlays. For example, while overall public expenditures on education in the late 2000s was about 4%, which is lower than both the average for the middle East and North Africa (MNA) region (4.5%) and that of the OECD (4.7%), the ratio of spending per student in higher education relative to pre-university education, at 3.2, is almost three times that of the OECD countries (Assaad, 2010). Youth from most privileged backgrounds (such as those from urban areas,

> 1 Campante and Chor (2012) argue that the mismatch between those educational gains and the lack of job opportunities contributed to the protests by decreasing the opportunity cost of time devoted to political activities

2
top wealth quintile, and parents with higher education) almost always attend university while those from most disadvantaged backgrounds (such as those from rural areas, lower wealth quintile, and less educated parents) have almost no chance of doing so.

Third, Egyptian pupils are tracked into general and vocational education at the senior secondary level, likely leading to divergence in educational and labor market outcomes. As the Egyptian education system relies on meritocratic selection into different education tracks, educational opportunities at the early stage could largely determine later education trajectories. Admission to general secondary and higher education institutions is based on performance in a high stakes national examination. A small minority of pupils, mainly from relatively better-off family backgrounds, meet the admission criteria, while a majority is tracked into vocational secondary schools <u>(Heyneman, 1997; World Bank, 2007). Private expenditures in tutoring play an important</u> role in students’ performance on national examinations.

Finally, existing empirical evidence shows large heterogeneity in schooling conditions for Egyptian youth with important implications on educational outcomes. In the early 1980s, <u>Loxley 1983 looked at the issue of the relative contribution of family and school inputs in learning</u> and found that school resources do matter and help pupils from poorer backgrounds and compensate to some extent for the lack of family inputs. Lloyd et al. (2003) have used data from a survey of young people and the schools they attend in the late 1990s to document the effects of schools’ inputs and environments on attainments. The study found that school conditions significantly affect schooling decisions.

This paper documents the level and evolution of inequities in access to education and educational achievements in Egypt. First, it analyzes the distribution of access to education and educational attainments, and the relationships between a set of circumstances, or characteristics determined at birth and lying outside a student’s control, and educational outcomes. Second, the paper measures the inequalities in learning achievements and their evolution across educational levels and generations of Egyptian youth. For this, three main sources of data are used: (a) Egypt’s 3
labor force surveys conducted in 1988, 1998 and 2006; (b) test-scores in Trends in International Mathematics and Science Study (TIMSS) in 2003 and 2007; and (c) national examination scores at completion of primary, preparatory and secondary education. Finally, the paper looks at the role of two potential factors of learning inequities: (a) the extent to which exam score differentials are associated with attendance of different school systems; and (b) the role of private expenditures in tutoring on learning outcomes.

The paper is structured as follows. Section 2 focuses on changes in educational attainments and their association with an individual background. Section 3 provides measures of the opportunity-shares of learning achievement inequalities. Section 4 provides empirical evidence on the contributions of attendance of schools from different systems and of private expenditures in tutoring to the formation of learning inequities. Section 5 concludes the paper.

# **2. Access to Education and Educational Attainment**

Here we look at access to education by individuals from different socioeconomic and geographic backgrounds over the last two decades. We use data from three labor force surveys: the Egypt Labor Force Sample Survey (LFSS) of 1988, the Egypt Labor Market Survey (ELMS) of 1998, and the Egypt Labor Market Panel Survey (ELMPS) of 2006.<sup>2</sup> The surveys are representative of the total population and consist of about 28,000, 24,000, and 37,000 individuals, respectively. They are based on similar sampling design and questionnaires, ensuring data comparability. The surveys collected information on educational attainments, measured by completed levels of education and school enrollment, as well as on gender, region of birth, parents’ educational attainments, and father’s occupational status. The paper looks specifically at a subsample of young people aged 21-24 years, most of whom have already completed their education.<sup>3</sup>

**Table 1** presents the completion rates at preparatory, secondary and college levels for 21-24

> 2 The LFSS 1988 was conducted by the Central Agency for Public Mobilization and Statistics (CAPMAS). The ELMS 1998 and ELMPS 2006 were conducted by the Economic Research Forum (ERF) in cooperation with CAPMAS.

> 3 A small number are still enrolled in college but this is not a concern to the extent that they already attained some higher education.

4
year-olds in 1988, 1998 and 2006. The completion rates at preparatory level increased steadily from 43 to 69 percent and at secondary from 38 to 65 percent over the same period. Access to higher education increased substantially with completion rates increasing from 7 to 17 percent. Nevertheless, tracking into technical-vocational and general secondary schools remained high over the period. As a result, the rate of graduation from the vocational secondary schools increased faster (from 24 to 42 percent) than from the general secondary schools (from 19 to 27 percent).

**Table 1: Completion rates of 21-24 year-olds by education level**

||**1988**|**1998**|**2006**|
|---|---|---|---|
|Preparatory|0.434|0.527|0.692|
|Secondary|0.379|0.480|0.645|
|General secondary|0.194|0.167|0.269|
|College|0.0668|0.111|0.172|

_Source:_ 1988 LFSS; 1998 ELMS; and 2006 ELMPS. Sample: 21-24 year-olds

**Table** 2 presents probit model estimates of the relationship between educational attainment and circumstances beyond the control of an individual. The effects of circumstance variables on completion of preparatory, general secondary and college education are substantial. Other things being equal, girls are less likely to complete basic education over the entire period (with differentials of 2 in 1988 and 1998 and 4 points in 2006). However, gender gaps have decreased at general secondary and college levels. The disadvantage of girls has disappeared over the period and even been reversed at secondary levels with girls 3 percent more likely than boys to complete general secondary education in 2006. Similarly, the disadvantage of girls at higher education levels appears to have reversed with a 2 percent higher college completion rate in 2006.

The differentials between youth of different family backgrounds also tend to decrease over time. For instance, the differential associated with the agricultural (or elementary) occupation of a father (13 percentage points in 2006) declined by more than twice, as did the differential associated with parental education. Similarly, although youth born in rural Lower and Upper Egypt are much less likely to complete basic education (respectively 23 and 15 percentage points less in 2006, 5
compared to youth born in Cairo), the gaps between rural and urban areas have decreased somewhat over the period (although the progress is limited in Upper Egypt). Thus the expansion of enrollment at basic education seems to have benefited youth from disadvantaged backgrounds and rural areas.

However, the gaps associated with parental background and region of birth have not diminished at upper education levels, where completion rates have increased mostly for kids from more privileged backgrounds. At the general secondary level, youth born in rural Upper Egypt are 14 and 18 percentage points less likely to attain a general secondary education in 1988 and 2006, respectively. Youth whose father has an agricultural (or elementary) occupation seem to be at a higher disadvantage in 2006 than in 1988, with 9 percentage points lower likelihood to attain a general secondary degree. Finally, the differentials associated with mother’s education persisted.

Similarly, at the college level, the differentials associated with region and family background persist and have even increased over the entire period. For instance, youth born in rural Upper Egypt are 11 percentage points less likely than those born in Great Cairo to attain a college education in 2006 (3 percentage points in 1988). Children of agricultural workers are 6 percentage points (4.5 percentage points in 1988) less likely than children of higher occupation fathers. Youth whose mothers are uneducated are 22 percentage points (11 percentage points in 1988) less likely than those whose mothers have attained secondary education. In general, while basic education has been democratized, barriers to access to general secondary and college education have remained for kids from disadvantaged backgrounds. This is a sign of the increased competition taking place for accessing general senior-secondary schools and universities.

6
**Table 2: Circumstances and completion rates: Probit estimates**

||Preparatory||Generalsec|ondary|College||
|---|---|---|---|---|---|---|
||1988|2006|1988|2006|1988|2006|
|Girl|-0.0216***<br>(0.001)|-0.0443***<br>(0.000)|-0.0229***<br>(0.000)|0.0266***<br>(0.000)|-0.0052***<br>(0.000)|0.0244***<br>(0.000)|
|Alexandria, Port Said, Suez|-0.0903***<br>(0.001)|-0.1297***<br>(0.001)|0.0072***<br>(0.001)|-0.0452***<br>(0.001)|-0.0173***<br>(0.000)|-0.0300***<br>(0.000)|
|Urban lower|<br>-0.0953***<br>(0.001)|<br>-0.0558***<br>(0.001)|<br>-0.0006<br>(0.001)|<br>-0.0589***<br>(0.001)|<br>0.0083***<br>(0.000)|<br>-0.0065***<br>(0.001)|
|Urban upper|<br>-0.0956***<br>(0.001)|<br>-0.0634***<br>(0.001)|<br>0.0016*<br>(0.001)|<br>-0.0595***<br>(0.001)|<br>-0.0017***<br>(0.000)|<br>-0.0488***<br>(0.000)|
|Rural lower<br>Rural upper|<br>-0.2222***<br>(0.001)<br>-0.2600***<br>(0.001)|<br>-0.1497***<br>(0.001)<br>-0.2316***<br>(0.001)|<br>-0.1134***<br>(0.000)<br>-0.1434***<br>(0.000)|<br>-0.1178***<br>(0.001)<br>-0.1795***<br>(0.000)|<br>-0.0454***<br>(0.000)<br>-0.0330***<br>(0.000)|<br>-0.0394***<br>(0.000)<br>-0.1068***<br>(0.000)|
|Mother w/ primary or preparatory<br>Mother w/ secondary<br>Mother w/ college|0.2960***<br>(0.002)<br>0.4146***<br>(0.003)|0.1651***<br>(0.001)<br>0.1656***<br>(0.001)<br>0.0461***<br>(0.001)|0.1168***<br>(0.001)<br>0.4360***<br>(0.002)<br>0.2303***<br>(0.004)|0.2272***<br>(0.001)<br>0.3632***<br>(0.001)<br>0.3564***<br>(0.002)|0.0362***<br>(0.001)<br>0.1053***<br>(0.001)<br>0.0190***<br>(0.001)|0.1338***<br>(0.001)<br>0.2164***<br>(0.001)<br>0.2484***<br>(0.001)|
|Father w/ primary or preparatory<br>Father w/ secondary<br>Father w/ college|0.1948***<br>(0.001)<br>0.3664***<br>(0.002)<br>0.5159***<br>(0.002)|0.1690***<br>(0.000)<br>0.1825***<br>(0.001)<br>0.0989***<br>(0.001)|0.1026***<br>(0.001)<br>0.2678***<br>(0.002)<br>0.5045***<br>(0.002)|0.1153***<br>(0.001)<br>0.1855***<br>(0.001)<br>0.2601***<br>(0.001)|0.0624***<br>(0.001)<br>0.0928***<br>(0.001)<br>0.1478***<br>(0.002)|0.0706***<br>(0.001)<br>0.1439***<br>(0.001)<br>0.1517***<br>(0.001)|
|Father service or industry worker|-0.0428***<br>(0.001)|-0.0745***<br>(0.001)|-0.0523***<br>(0.001)|-0.0856***<br>(0.001)|-0.0193***<br>(0.000)|-0.0781***<br>(0.000)|
|Father in agriculture or elem. ocup.|<br>-0.2997***<br>(0.001)|<br>-0.1259***<br>(0.001)|<br>-0.0844***<br>(0.001)|<br>-0.0941***<br>(0.001)|<br>-0.0451***<br>(0.000)|<br>-0.0608***<br>(0.000)|
|Number of Obs|3,255,266|6,350,947|3,298,129|6,350,947|3,298,129|6,350,947|
|Pseudo R2|0.199|0.127|0.222|0.237|0.183|0.212|

_Source:_ 1988 LFSS; 1998 ELMS; and 2006 ELMPS. _Sample:_ 21-24 year-olds; reference categories are: boy, birth in Great Cairo, uneducated mother (or missing value), uneducated father (or missing value), and father professional, manager, technician or clerck. p<0.10, ** p<0.05, *** p<0.01;

# _Least and most advantaged groups_

In addition to the descriptions given above, there are other informative ways to illustrate the stark disparities among circumstance groups. One way is to compare the characteristics of students belonging to the extremes of the distribution of educational achievements. **Table 3** describes the characteristics of the youth who did not complete basic education and of those who completed college education. Uneducated youth consist in more than 1,300,000 individuals aged 21-24 in 2006 (that number decreased only slightly from 1,500,000 in 1988), and the share of girls increased 7
among them to reach two-thirds for that year. They tend to be more concentrated in rural areas (more than 80 percent in 2006), are in great majority children of uneducated parents (more than 90 percent in any year), and children of agricultural (or elementary) workers (more than 60 percent in 2006). At the other end, college graduates vastly increased from about 220,000 individuals aged 2124 in 1988 to more than 1,100,000 in 2006; girls became the majority of them (57 percent in 2006). While the share of college graduates in rural Lower Egypt increased, no increase was observed in rural Upper Egypt. The shares of individuals from more disadvantaged backgrounds diminished among college graduates. For instance the share of children from agricultural or elementary workers diminished from 46 to 26 percent, while the share of children of higher occupation fathers increased from 35 to 55 percent.

**Table 3: Characteristics of those with low and high educational attainment**

|||**No schooling**||**Co**|**llege educat**|**ion**|
|---|---|---|---|---|---|---|
||<br>**1988**|<br>**1998**|<br>**2006**|<br>**1988**|<br>**1998**|<br>**2006**|
|female|0.538|0.547|0.642|0.440|0.534|0.570|
|Great Cairo|0.132|0.0717|0.0484|0.485|0.346|0.210|
|Alex, Suez, P. Said|0.0751|0.0315|0.0344|0.0912|0.119|0.108|
|Urban lower|0.0733|0.0560|0.0516|0.146|0.127|0.167|
|Urban upper|0.0343|0.0462|0.0588|0.0890|0.0784|0.146|
|Rural lower|0.365|0.360|0.387|0.0982|0.255|0.272|
|Rural upper|0.320|0.434|0.420|0.0904|0.0753|0.0973|
|Father w/o schooling (or missing)|0.982|0.922|0.928|0.481|0.343|0.311|
|<br>Father w/ basic education|0.0135|0.0699|0.0379|0.159|0.137|0.133|
|Father w/ secondary education|0.00449|0.00611|0.00863|0.188|0.189|0.300|
|Father w/ college education|0|0.00215|0.0251|0.173|0.331|0.256|
|Mother w/o schooling (or missing)|0.994|0.984|0.955|0.665|0.505|0.446|
|Mother w/ basic education|0.00556|0.0156|0.0173|0.120|0.187|0.148|
|Mother w/ secondary education|0|0|0.0158|0.155|0.208|0.290|
|Mother w/ college education|0|0|0.0116|0.0595|0.101|0.116|
|Profes., manag., techn., clerks|0.0270|0.102|0.0936|0.352|0.497|0.554|
|Service, craft, and industry workers|0.113|0.267|0.286|0.185|0.181|0.184|
|Agriculture, elem. occup., missing|0.860|0.631|0.621|0.463|0.322|0.262|
|Population|1,492,612|953,225|1,345,590|220,180|496,557|1,089,725|

_Source:_ 1988 LFSS; 1998 ELMS; and 2006 ELMPS. Sample: 21-24 year-olds Another way is to group students into least and most advantaged based on their individual circumstances. Here we define least advantaged youth as those with unfavorable circumstances, such as being born in rural areas, from uneducated parents, and having a father with elementary or 8
<!-- Start of picture text --> Most disadvantaged Most advantaged<br>lo} fo}<br>oe) [oe}<br>67 66<br>60<br>lo}© fo}Oo<br>49 48<br>: 1 41<br>9 37 38 =) “<br>29<br>23<br>a 18 &<br>13<br>8<br>5<br>1 22<br>lo} QO 1-8 9-11 12+ QO 1-8 9-11 12+ O 1-8 9-11 12+ fo} O 1-8 9-11 12+ O 1-8 9-11 12+ O 1-8 9-11 12+<br>1988 1998 2006 1988 1998 2006<br>Most disadvantaged: born in rural lower or upper Egypt, uneducated parents, father in agriculture or<br>elementary occupation.<br>Most advantaged: born in Great Cairo, Alexandria, Suez, or Port Said, parents with secondary or college,<br>higher occupation father.<br><!-- End of picture text -->
and many of them were able to accessed basic education. The share of college graduates increased by 17 points among most advantaged, while there was no appreciable increase among the least advantaged.

# _Drop-out rate_

A similar picture emerges when looking at the drop-out rate among those who participated in the 2007 TIMSS survey for 8<sup>th</sup> graders. Consider 15-19 year-olds in 2006, most out-of-school children actually never enrolled (about 13 percent of 15-19 year-olds in 2006) or dropped out after obtaining some vocational secondary education (15 percent). Drop-outs after primary (7 percent) or preparatory (4 percent) are less frequent. **Table 4** reports results from a partial correlations analysis of drop-out, i.e. probit estimates of the marginal effects of a set of circumstance variables for the same samples of 15-19 year-olds in 1988 and 2006. Girls have more often dropped out than boys, but the gap decreased from 23 to 8 percentage points between 1988 and 2006. While large geographic differentials persist in drop-out rate, there have been continuous improvements over the last two decades. Children born in rural Upper Egypt continue more likely to drop out than those born in any other region but the gap decreased from 21 to 9 percentage points, compared to Greater Cairo. However some gaps associated with family background have only slightly diminished, e.g. children with uneducated parents are still much more likely to have dropped out in 2006 (about 30 percentage points more than children of college graduates).

10
**Table 4: Circumstances and drop-out rate: Probit estimates**

||**1988**|**1998**|**2006**|
|---|---|---|---|
|Girl|0.2268***<br>(0.000)|0.0813***<br>(0.000)|0.0777***<br>(0.000)|
|Alex, P.Said, Suez|0.0951***<br>(0.001)|0.1189***<br>(0.001)|0.0592***<br>(0.001)|
|Urban lower|0.0663***|0.1483***|-0.0339***|
||(0.001)|(0.001)|(0.001)|
|Urban upper|-0.0015<br>(0.001)|0.0944***<br>(0.001)|-0.0036***<br>(0.001)|
|Rural lower|0.1679***<br>(0.001)|0.1223***<br>(0.001)|-0.0251***<br>(0.001)|
|Rural upper|0.2055***<br>(0.001)|0.1416***<br>(0.001)|0.0932***<br>(0.001)|
|Mother w/ primary or preparatory|-0.2383***<br>(0.001)|-0.1839***<br>(0.001)|-0.1610***<br>(0.001)|
|Mother w/ secondary|-0.3488***<br>(0.001)|-0.2936***<br>(0.001)|-0.2790***<br>(0.000)|
|Mother w/ college|||-0.3128***<br>(0.001)|
|Father w/ primary or preparatory|-0.2032***<br>(0.001)|-0.0954***<br>(0.001)|-0.0608***<br>(0.000)|
|Father w/ secondary|-0.2196***<br>(0.001)|-0.1365***<br>(0.001)|-0.1234***<br>(0.001)|
|Father w/ college|-0.3563***<br>(0.001)|-0.1594***<br>(0.001)|-0.2776***<br>(0.001)|
|Father service or industry worker|0.0441***<br>(0.001)|0.1026***<br>(0.001)|0.0310***<br>(0.001)|
|Father in agriculture or element. ocup.|0.0544***<br>(0.001)|0.1349***<br>(0.001)|0.0287***<br>(0.001)|
|Number of Obs|5075557|7396773|7812837|
|PseudoR2|0.123|0.094|0.136|

_Source_ : 1988 LFSS; 1998 ELMS; and 2006 ELMPS. _Note:_ * p<0.10, ** p<0.05, *** p<0.01. _Sample_ : 15-19 year-olds; reference categories are: boy, birth in Great Cairo, uneducated mother (or missing value), uneducated father (or missing value), and father professional, manager, technician or clerk.

**Table 5** documents the reasons reported for never attending school and dropping out (using

information collected among 10-29 year-olds by the SYPE 2009 survey). About half of neverattenders report family or own choice as a reason for not going to school. A higher proportion for girls reported that cultural views toward gender roles still enter in these decisions. Financial constraints and opportunity costs of schooling (i.e. the foregone economic contribution of children attending school) were mentioned by 52 and 46 percent of never-attending boys and girls, respectively. Customs, traditions and marriage are cited as reasons by 24 percent of girls. Hence, 11
when availability of schools is not a prominent reason, lack of financial resources and cultural attitudes toward girls’ schooling are the main reasons reported for never attendance. Similarly, financial constraints and opportunity costs are cited for dropping out by 60 and 39 percent of boys and girls, respectively. Avoiding mixing with the opposite gender or marriage was cited by 11 percent of girls for dropping out.

**Table 5: Reasons for never attending and dropping out of school**

||_Never A_<br>|_ttendance_<br>|_Dro_<br>|_p-out_<br>|
|---|---|---|---|---|
||**Males**|**Females**|**Males**|**Females**|
|Choice|0.472|0.560|0.581|0.518|
|Financial constraints|0.319|0.320|0.148|0.233|
|Opportunity cost|0.293|0.144|0.447|0.158|
|School supply|0.021|0.055|0.012|0.028|
|Customs and traditions or<br>marriage|0.025|0.241|||
|Mistreatment by teachers and<br>discipline|||0.024|0.041|
|Health issues|0.194|0.0364|0.025|0.020|
|Achievements|||0.187|0.117|
|No birth certificate|0.012|0.009|||
|Avoid gender mixing or<br>marriage|||0.002|0.115|
|Observations|225|933|478|729|

_Source:_ SYPE 2009. Sample: Those never attended and ages 10-29 years.

# **3. Inequities in Learning Achievements**

In Section 2, we showed that while there has been democratization in access to basic education in Egypt, there remain persistent disparities in access to general secondary and college education. Given that educational trajectories depend to a large extent on tracking into general and vocational secondary schools at completion of basic education, the quality of the education at lower levels of schooling is likely to be a major determinant of educational inequalities in Egypt.<sup>4</sup> We now turn to inequalities in learning achievements, as measured by TIMSS evaluation of pupils’

> 4 The tracking is determined by scores at the preparatory completion exam organized at the level of governorate.

12
achievements, at the end of basic education (8<sup>th</sup> grade). For investigating learning achievements at completion of basic education, we use information from the TIMSS conducted in Egypt in 2003 and 2007. These surveys collected responses, from a representative sample of Egyptian eighth-8<sup>th</sup> graders, to series of items of a test-based evaluation of achievements in Math and Science. 7,095 pupils from 217 schools in 2003 and 6,582 pupils from 233 schools in 2007 were interviewed.<sup>5</sup> The TIMSS surveys collected information on children’s background, in particular, in addition to their gender, mother’s and father’s education, the language spoken at home, whether any of the parents was born abroad, household ownership of durables such as books, tv sets, satellite channels access systems, and phones. The information on the economic status of the parents is limited though, with parental, notably father’s, educational attainments serving as an imperfect proxy. Besides, the surveys indicate the location of the schools, which we take as a proxy for places where children were raised.<sup>6</sup> For the 2007 survey, we could use information on the governorate and area type (urban or rural) of the school location. For the 2003 survey, we could only access the area type information.<sup>7</sup>

# _Methodology for measuring learning opportunity inequality_

Achievements in the TIMSS Math and Science tests are measured using item response theory models, aggregating answers from many test items of varying difficulty and discriminating power. These models treat achievement as a latent variable, and, assuming a given distribution (usually a normal) for the achievements of pupils, their estimation allows deriving its distribution and its predicted value for each individual (see <u>Mislevy, 1991; Mislevy et al., 1992). Importantly, these</u> models recover estimates of individual achievements that come with a scale that is arbitrarily fixed.

> 5 We report the results for Math test-scores. The results for Science are very similar and available upon request.

> 6Migrations motivated by studies should be limited among 8 graders. The SYPE survey provides no information on birth place of children less than 15.

> 7The definition of urban areas is not perfectly consistent across the 2003 and 2007 surveys. The 2003 information relies on a classification of “communities” by population size (communities with more than 50,000 inhabitants are classified as urban), while the 2007 information is the official classification consists in the administrative classification (which was not available in 2003).

13
Essentially, there is no scale for measuring a pupil’s scholastic ability, so that some reference levels have to be fixed for the mean and standard deviation of achievement measured by a test. International achievement tests thus fix these reference levels of mean and standard deviation for a given set of country and year, and the subsequent evaluations conducted with the same test are reported using the same arbitrary scale. In the TIMSS procedure, test -scores were normalized by fixing the mean and standard deviation of achievements of pupils who participated to the first 1995 international TIMSS survey at, respectively, 500 and 100. This standardization must be dealt with in inequality measurement.

The inequality measures to be used for documenting the distributions of test scores must account for several methodological issues. In particular they must be robust to the standardization of these TIMSS variables. However, as noted by Ferreira and Gignoux (2011a), no inequality index satisfying a set of basic desirable properties (symmetry, continuity and the transfer principle) can be both scale- and translation invariant, and thus robust to the standardization. A number of wellknown inequality indices, such as the Gini and Generalized Entropy indexes, are not even ordinally equivalent, so that the arbitrary standardization could affect the ranking of different population by inequality levels.

For handling this standardization issue, we follow Ferreira and Gignoux (2011a), and base our inequality analysis on the variance (and standard deviation). Consider a post-standardized distribution of test-scores (yij), obtained as a linear function of the pre-standardization distribution (xij): The variance (or standard deviation) of a post-standardized distribution (Vj<sup>y</sup> ), for example, is a monotonic (proportional) function of the pre-standardization variance (Vj<sup>x</sup> ), and does not depend on any other moment of the pre-standardization distribution; for the variance: 14
While it is not scale invariant, the mean (as the standard deviation) is thus ordinally-invariant to the standardization. In addition it satisfies the basic properties above (and is also additively decomposable). It thus provides a basic standardization ordinally consistent measure of inequality of educational achievement. In practice, we report the standard deviation.

In this paper, rather than overall inequality, we are mainly interested in inequality of achievement opportunity. Following <u>Roemer (1998), empirical analyses of the distribution of</u> opportunities begin by seeking agreement on a set of characteristics which are beyond the individual’s control, and for which he or she cannot be held responsible. These variables are called ‘circumstances’. Once a vector C of circumstances has been agreed upon, the population can be partitioned into groups with identical circumstances.

Given such partition, in one approach to the measurement of inequality of opportunity, called ex-ante and associated with <u>van de Gaer</u> (1993), the opportunity set faced by each type is evaluated using the conditional distributions of outcomes given circumstances, and equality of opportunity is attained when there is perfect equality in these values across all types. This rests on the notion that purely individual effort or ability should be orthogonal to an individual’s circumstances at birth (for more discussion, see e.g. <u>Roemer, 1998; Fleurbaey, 2008).</u><sup>8</sup> The full conditional distributions of outcomes given types or some of its higher moments can be used, but, in practice, researchers have often used the mean income (or achievement) of the type as an estimate of the value of the opportunity set they face. Since equality of opportunity would imply equality in means across types, inequality of opportunity is then naturally seen as some measure of between-type inequality. As in <u>Ferreira and Gignoux (2011a, b), we thus measure inequality of</u> opportunity (IOp) by between-type inequality, or specifically:

> 8In an alternative but closely related approach, called ex-post and associated with <u>Roemer</u> (1998), equality of opportunity obtains only when individuals exerting the same degree of effort, regardless of their circumstances, receive the same reward. See Fleurbaey and Peragine (forthcoming) for a formal discussion of the relationship between the two approaches.

15
where is the smoothed distribution corresponding to the distribution y and the partition P.

Following <u>Bourguignon et al. (2007) and Ferreira and Gignoux (2011b), one can obtain a</u> parametric estimate for , based on an OLS regression of y on C: in (4) is the OLS estimate of the regression coefficients in a simple regression of y on C: In (4), the denominator is a function of the vector of predicted incomes from regression (5). Under the maintained assumption of a linear relationship between achievement and circumstances, this vector is equivalent to the smoothed distribution, since all individuals with identical circumstances are assigned their conditional mean incomes.

Following the arguments above, we use the simple variance as our inequality index I(). This choice yields our proposed measure of inequality of educational opportunity, as a special case of <u>(4):</u> This is the R-square of an OLS regression of the child’s test score on a vector C of individual circumstances. Despite its simplicity, it is a meaningful summary statistic. It is a parametric approximation to the lower bound on the share of overall inequality in educational achievement that is causally explained by the joint effect of all circumstances. A formal proof is provided by Ferreira <u>and Gignoux (2011b). But the basic intuition is that some circumstances remain unobserved and</u> observing and controlling for these unobservables could only increase the share of explained inequalities. However, the estimates should not be interpreted as capturing the effects of the specific 16
set of circumstances considered in the analysis as they may reflect the effects of omitted and correlated individual and environmental characteristics. Individual elements of the vector thus suffer from these omitted variable biases, and cannot be interpreted as causal estimates of the individual impact of a particular circumstance on test scores.

Another attractive feature of as a measure of inequality of educational opportunity is that, unlike any measure of the level of inequality, it is a parametric estimator of a ratio (equation <u>(3)) that is cardinally invariant in the standardization of test scores. To see this, note that any sub-</u> group mean is affected by standardization in a manner analogous to equation (1), so that: Given (7) and equation (2), it follows that: Finally, is decomposable into components for each individual variable in the vector C. Equation (6) can be rewritten as the sum over all elements (denoted by j) of the C vector:<sup>9</sup> Beyond the specific measures to be used, an additional challenge is that the TIMSS sample is representative of eighth-graders and not of the overall population of lower-secondary school age children. An estimate of the share of preparatory-level graduates among these cohorts can be obtained from the SYPE survey: 78 percent of youth between 16 and 21 complete preparatory schooling. Thus 22 percent drop out before and their achievements are not reflected in a basic analysis of TIMSS test-scores.<sup>10</sup> This selection is problematic for an analysis of educational

> 9This is an example of a Shapley-Shorrocks decomposition: it corresponds to the average between two alternative paths for estimating the contribution of a particular circumstance CJ to the overall variance. In the first (direct) path, all _Cj, j ≠ J_ are held constant. In the second (residual) path, Cj is itself held constant, and its contribution is taken as the difference between the total variance and the ensuing variance. Either path is conceptually valid, and the Shapley-Shorrocks averaging procedure yields (8) as the path-independent additive decomposition. See Shorrocks (1999) for the original application of the Shapley value to distributional decompositions. Ferreira and Gignoux (2011a) provide a formal proof that (8) is the Shapley-Shorrocks decomposition of the variance into the effects of individual circumstances.

> 10Among those, 6.5 percent have never been to school, 4.5 dropped out before completing primary, and 11 percent

17
opportunities which is not concerned only in the outcomes of preparatory-level enrollees. Dropping out is extremely likely to be a selective process, in the sense that it is correlated with family and student characteristics that also affect test scores.

In this paper, we have attempted to correct for the selection biases described above. We follow Ferreira and Gignoux (2011a), and use a simple two-sample non-parametric mechanism for assessing the sensitivity of our inequality measures to alternative assumptions about the sample selection process. The procedure relies on using information on preparatory school graduates from the HIECS Egyptian budget survey from 2004-05. This large-sample survey allows estimating the numbers of preparatory-school graduates and drop-outs in groups defined by similar gender, father’s education and region of residence (the two later variables have consistent coding in the two surveys). The procedure then consists in putting back observations and imputing test-scores for drop-outs in the TIMSS sample. Two extreme assumptions are used for the imputations. The first assumes selection on observables whereby drop-outs would achieve similarly to children taking the TIMSS tests that have similar characteristics. The second assumes selection on both observables and (strongly) unobservables and attributes a test-score in the lower tail (we use the 5th quintile) of the conditional distribution of scores achieved by test takers with similar characteristics. In practice, the corrections are implemented by a re-weighting of the TIMSS sample.

# _Learning achievement inequality: low mean and high dispersions_

By applying the measures above to the TIMSS data, we first document the levels of inequality in learning achievement observed in Egypt and put them in an international perspective. **Figure 2** plots the country-level standard deviations of test scores against the mean scores in Math in the TIMSS 2007 data. Egypt, with a mean score of 391 and a standard deviation of 100, lies in the top left quadrant, i.e. has both a low mean and a high dispersion in learning achievements.

before completing preparatory.

18
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2007 and those of rural Upper Egypt almost 75 points below those in urban Lower Egypt.

**Table 6: OLS Regressions of Standardized Math Test Scores on Circumstances**

||**2003**<br>**urban/rural**|**urban/rural**|**2007**<br>**six regions**|**governorate**|
|---|---|---|---|---|
|female|-0.1312|11.2214***|<br>7.5360***|<br>3.0034|
||(2.324)|(2.756)|(2.730)|(2.672)|
|father w/ basic education|<br>3.7598|<br>9.8207**|<br>6.4850|<br>3.0275|
||(4237)|(4772)|(4630)|(4461)|
|father w/ secondary|.<br>8.0609*|.<br>24.8174***|.<br>23.8173***|.<br>20.0579***|
||(4.319)|(5.862)|(5.777)|(5.578)|
|father w/ post-secondary|28.9035***|37.7932***|35.9247***|34.2455***|
||(6.137)|(5.915)|(5.803)|(5.623)|
|father w/ college|<br>44.1157***|<br>14.5372**|<br>12.9275**|<br>10.9477*|
||(5.745)|(6.452)|(6.465)|(6.357)|
|father edu. missing|-13.9200***<br>(4.769)|-5.7182<br>(5.657)|-6.0043<br>(5.591)|-8.3223<br>(5.429)|
|mother w/ basic education|<br>-6.2337|<br>-6.1142|<br>-6.3941|<br>-7.0917*|
||(3.886)|(4.331)|(4.235)|(4.134)|
|mother w/ secondary|<br>-5.5929|<br>12.5279**|<br>9.4317*|<br>7.9994|
||(4.223)|(5.491)|(5.417)|(5.268)|
|mother w/ post-secondary|21.976***|16.6585***|16.914***|12.757**|
||(5.639)|(5.935)|(5.875)|(5.791)|
|mother w/ college|<br>35.1450***|<br>-9.9665|<br>-8.7409|<br>-9.2952|
||(5.750)|(6.319)|(6.377)|(6.238)|
|mother edu. missing|-17.7730***|-12.4057**|-11.0279**|-10.9768**|
||(4.346)|(5.010)|(5.082)|(4.963)|
|parent(s) born abroad|-24.7708***|-42.2089***|-42.0816***|-46.6665***|
||(2.996)|(3.799)|(3.733)|(3.578)|
|arabic spoken: almost always|13.8799***|27.9687***|27.1901***|24.7760***|
||<br>(3.516)|<br>(4.206)|<br>(4.109)|<br>(4.091)|
|arabic spoken: sometimes|<br>82022***|<br>142392***|<br>166867***|<br>157405***|
||.<br>(2.950)|.<br>(3.761)|.<br>(3.722)|.<br>(3.718)|
|arabic spoken: never|-14.3242**|-7.5273|-3.8754|-0.0855|
||(7.076)|(7.928)|(8.352)|(7.913)|
|books at home: 11-25|<br>-1.1348|<br>-3.0248|<br>-1.3487|<br>-0.5250|
||(2.791)|(3.384)|(3.313)|(3.218)|
|books at home: 26-100|<br>9.0014**|<br>6.3848|<br>9.0688**|<br>9.0645**|
||(3.746)|(4.045)|(3.967)|(3.921)|
|books at home: >100|4.9763|1.4673|6.3031|7.1206|
||(4.466)|(5.062)|(5.006)|(4.917)|
|tv owned|45.9552***|26.5948***|22.6609***|24.5407***|
||(4.640)|(5.245)|(5.167)|(5.069)|
|satelite owned|-13.6241***|6.0568**|7.9091***|6.9546**|
||(2.395)|(2.950)|(2.896)|(2.825)|
|telephone owned|<br>67064**|<br>102867***|<br>92801**|<br>89792**|
||.<br>(2.726)|.<br>(3.816)|.<br>(3.729)|.<br>(3.677)|
|urban area|14.2031***|35.3493***||37.8641***|
||(2.424)|(2.949)||(3.549)|
|Alx Sz Said|||3.6656||
|, ,|||<br>(4.929)||
|Urban lower|||<br>19.0216***<br>||
|Ub|||(4.933)<br>49274||
|ran upper|||-.||
||||(4.878)<br>||
|Rural lower|||-7.0962<br>(4353)||
|Rural upper|||.<br>-55.3107***||
||||(4378)||
|Governorates|No|No|.<br>No|Yes|
|Constant|350.0780***|325.2088***|358.6597***|269.8665***|
||(5.502)|(6.256)|(7.098)|(13.278)|
|Number of Obs|7095|6582|6582|6582|
|<br>R-squared|<br>0.210|<br>0.181|<br>0.210|<br>0.239|

_Source: TIMSS 2003 and 2007 surveys._ 21
The estimates of the opportunity shares of inequalities in achievements at the TIMSS Math test are shown in **Table 7** . The results for 2007 (column 2) with a similar urban-rural specification give a lower bound for the opportunity share at 18.2 without the corrections for potential selection bias. It is lower than the corresponding figure in 2003 by 3.4 percent; the decrease has a standard error of .85 point, a statistically significant difference. It is worth noting that the 2003 and 2007 estimates are not perfectly comparable for reasons described earlier. However, the ranges obtained with the imputations for selection bias 2003 and 2007 overlap.

The two remaining columns use more detailed information on geography. The lower bound increases to 21.6 percent with the six regions school location variable (and vary between .213 and .266 with the imputations) and to 24.2 percent when accounting for the precise governorates for school locations. This increase suggests that achievement inequalities associated with geography are large. Overall, based on the most complete estimates in the last column, at least a quarter of achievement inequalities at the end of preparatory can be attributed to circumstances. This figure is comparable to those from similar analysis in other countries (e.g., the share of inequality of opportunity was found similar in countries such as UK or the US in Ferreira and Gignoux, 2012). Bu given the high overall learning inequalities, the absolute level of learning inequities at completion of basic education is higher in Egypt than in many other countries.

The estimates of partial shares associated with specific circumstances confirm the importance of geography. Using data on Math test-scores in 2007, 4.5 percent of inequalities are accounted for by area type in the first model, 8.3 percent by the six regions indicators in the second, and 11.2 percent by area type and governorate combined in the third model. In comparison, the next most important circumstances are parental education (3 to 4 percent for father’s and about 2 percent for mother’s education), and immigration status (3 to 4 percent), and ownership of cultural goods which accounts for another 3 percent. Language at home accounts for about 1.5 percent. Gender is not associated with a significant share of inequalities in achievements.

22
**Table 7: Opportunity Share of Inequality in Educational Achievement**

||**2003**||**2007**||
|---|---|---|---|---|
||**urban/rural**|**urban/rural**|**six regions**|**governorate**|
|Overall inequality|8603.9|10050.1|10050.1|10050.1|
|sd|153.9|180.9|180.9|180.9|
|Inequality of opportunity: level|1858.8|1832.2|2169.5|2431.7|
|sd|67.11|88.23|94.08|89.58|
|Inequality of opportunity: share|0.216|0.182|0.216|0.242|
|sd|0.00529|0.00667|0.00625|0.00571|
|_Higher alternative correction_|||||
|Overall inequality|10958.8|13646.5|13335.6||
|sd|230.3|354.6|423.1||
|Opportunity share|0.289|0.230|.266||
|sd|.006|0.005|.006||
|_Lower alternative correction_|||||
|Overall inequality|8057.6|9956.2|10130.81||
|sd|135.3|187.3|192.9554||
|Opportunity share|0.187|0.163|.213||
|sd|.00685|0.00642|.00717||
|Gender|0.0000446|0.00301|0.00177|0.000207|
|sd|0.000144|0.00114|0.000915|0.000688|
|Father’s education|0.0819|0.0385|0.0373|0.0367|
|sd|0.00484|0.00207|0.00200|0.00197|
|Mother’s education|0.0633|0.0204|0.0190|0.0165|
|sd|0.00318|0.00178|0.00173|0.00174|
|Immigration status|0.0144|0.0329|0.0329|0.0361|
|<br>sd|0.000922|0.00264|0.00246|0.00231|
|Language at home|0.00742|0.0148|0.0149|0.0136|
|sd|0.00110|0.00125|0.00129|0.00138|
|Books at home|0.00633|0.00436|0.00571|0.00554|
|sd|0.00137|0.000944|0.00101|0.000997|
|TV-satelite-phone|0.0312|0.0231|0.0214|0.0214|
|<br>sd|0.00205|0.00345|0.00342|0.00325|
|Urban-rural|0.0114|0.0452||0.0483|
|sd|0.00186|0.00334||0.00310|
|Six regions|||0.0828||
|sd|||0.00594||
|Governorates|0|0|0|0.0636|
|sd|0|0|0|0.00646|

_Source: TIMSS 2003 and 2007 surveys. Sample: TIMSS 2003 and 2007 samples of eight-graders._

# _Significant achievement inequality in national exams_

To investigate how learning opportunities evolve across schooling levels, here we expand the analysis to achievements at national exams at primary, preparatory, and secondary levels. For this use, we use retrospective data from the Survey of Young People of Egypt in 2009 (Asia and Office, 2011). The survey collected detailed information from a sample of 15,029 individuals aged 10 to 21. Notably, it asked information on whether individuals completed different schooling levels, and their scores in the national examinations at completion of the primary, preparatory, and 23
secondary levels. Examination scores take values ranging between 50 and 100. The average score is 82.0 (with standard deviation of 11.5) at the primary, 75.2 (13.2) at the preparatory, and 74.9 (12.8) at secondary levels.<sup>11</sup> We focus on scores of 12-15 year-olds at primary, 14-18 year-olds at the preparatory, and 18-21 year-olds at the secondary completion. Among these individuals, respectively, 59, 72 and 82 percent reported exam scores in the 2009 SYPE.

In addition to exam scores, the 2009 SYPE survey collected information on the background of the youth. In the analysis below, the set of variables used to capture their background include gender, governorate of birth, parental education level<sup>12</sup> , religion (Muslim vs another religion), and family wealth, obtained from information on the durables owned by households and housing conditions, and their wealth group (i.e., consumption quintiles).

We perform the analysis of inequality in exam scores using the same approach as for learning achievements evaluated using TIMSS data above. **Table 8** reports OLS estimates of the partial effects of circumstances on exam scores. Girls tend to do better at all three exams with a gap of 2 to 4 more exam points on average, increasing with attainment, than boys. The gaps associated with parental education levels are large, ranging from 6 points for father’s education at the primary completion exam to 10 points for mother’s education at the preparatory. The gaps associated with family wealth are large—about 8 points at the primary and preparatory completion exams. There are no apparent differentials in scores between Muslims and non-Muslims. However, there are large differentials associated with birthplace, with scores for some birth-governorates 13-14 points higher than others at the primary and preparatory completion levels (estimates not reported, available upon request).<sup>13</sup>

> 11 In an alternative but closely related approach, called ex-post and associated with <u>Roemer</u> (1998), equality of opportunity obtains only when individuals exerting the same degree of effort, regardless of their circumstances, receive the same reward. See Fleurbaey and Peragine (forthcoming) for a formal discussion of the relationship between the two approaches.

> 12Note that the information on parental education is missing for children who do not live with their parents. For those children, parental attainment is coded in a missing category (we do not drop those observations).

> 13Interestingly, the gaps by birth place are lower at the secondary level, suggesting that the pupils who attain that level of education are already quite selected.

24
**Table 8: OLS Regressions of national Exam Scores and Circumstances**

||**Primary**<br>**12-15 y.o.**|**Preparatory**<br>**14-18 y.o.**|**Secondary**<br>**17-21 y.o.**|
|---|---|---|---|
|Female|2.1457***|2.3074***|3.7964***|
||(0.594)|(0.665)|(0.548)|
|Father’s education: primary|1.9986*|1.3525|0.0715|
||(1.186)|(1.191)|(1.005)|
|Father’s education: Preparatory/Secondary|3.0621**|0.0195|0.7825|
||(1.416)|(1.440)|(1.173)|
|Father’s education: Vocational|4.3131***|3.0546**|-0.2084|
||(1.229)|(1.326)|(1.154)|
|Father’s education: Post-secondary|6.3862***|3.7641**|3.0193**|
||(1.282)|(1.469)|(1.277)|
|Father’s education: missing|4.2317***|1.5136|0.6747|
||(1.293)|(1.257)|(1.062)|
|Mother’s education: primary|1.1897|-0.0117|-0.8078|
||(1.118)|(1.142)|(0.812)|
|Mother’s education: Preparatory/Secondary|0.2962|2.5697**|0.6547|
||(1.247)|(1.159)|(1.166)|
|Mother’s education: Vocational|2.3152**|5.9249***|3.6505***|
||(1.064)|(1.146)|(1.031)|
|Mother’s education: Post-secondary|4.6120***|9.8281***|8.0822***|
||(1.254)|(1.318)|(1.273)|
|Mother’s education: missing|-2.6107*|0.9920|-0.8473|
||(1.485)|(1.648)|(1.134)|
|Wealth quintile: second|2.0688*|-0.5497|1.2235|
||(1.212)|(1.396)|(1.051)|
|Wealth quintile: middle|3.2326***|0.1682|2.3500**|
||(1.148)|(1.329)|(1.016)|
|Wealth quintile: fourth|4.9018***|2.4809*|3.2626***|
||(1.204)|(1.358)|(1.055)|
|Wealth quintile: highest|7.7830***|6.5910***|5.5169***|
||(1.260)|(1.514)|(1.249)|
|Religion: Non-Muslim|-0.3049|-0.3096|0.5954|
||(1.568)|(1.681)|(1.052)|
|Birth Governorates|<br>Yes|<br>Yes|<br>Yes|
|Constant|72.9671***|71.7394***|62.3858***|
||(1.829)|(2.999)|(2.580)|
|Number of Obs|1497|1525|1718|
|R-squared|0.244|0.306|0.224|

_Source:_ SYPE 2009 survey.

_Sample:_ 12-14 y.o. primary, 14-18 y.o. preparatory, and 17-21 y.o. secondary graduates. _* p<0.10, ** p<0.05, *** p<0.01_ The estimates of opportunity shares in the distribution of exam scores are shown in **Table 9** .

For scores at the primary completion exam, the set of circumstances used explains 24.4 percent of the overall variation in scores. This estimate varies very little with the lower alternative correction for selection, and increases to 29.1 percent with the upper-bound alternative. For scores at the preparatory completion, the uncorrected estimate is at 30.2 percent, and varies between 27.4 and 35.2 percent with the correction for selection. Finally, for scores at the secondary completion, the 25
uncorrected estimate is at 22.4 percent, and varies between 20.8 and 27.9 percent with the corrections.

The pattern of increasing and, later on, decreasing shares of inequality explained by circumstances may be explained by selection, which is likely to be larger at the secondary level. But the main result here is that learning opportunity inequality appears at early ages: a large amount of it (at least a quarter of achievement inequality) is already observed at the primary level. These inequities then build up to reach at least a third of learning inequality at the end of preparatory. Hence, tracking at the end of preparatory does not seem to explain all subsequent inequalities.

**Table 9: Opportunity Share of Inequality in National Exam Scores**

||**Primary**<br>**12-15 y.o.**|**Preparatory**<br>**14-18 y.o.**|**Secondary**<br>**17-21 y.o.**|
|---|---|---|---|
|Overall inequality|134.0|180.0|131.4|
|Inequality of opportunity: level|32.70|55.05|29.45|
|Inequality of opportunity: share|0.244|0.306|0.224|
|Overall inequality - lower correction|137.8|182.1|121.4|
|<br>Inequality of opportunity: share - lower correction|0.238|0.274|0.208|
|Overall inequality - upper correction q5|218.3|194.2|156.7|
|Inequality of opportunity: share - upper correction q5|0.291|0.352|0.279|
|Gender|0.00769|0.00809|0.0266|
|Father’s education|0.0633|0.0387|0.0286|
|Mother’s education|0.0563|0.117|0.0909|
|Wealth|0.0785|0.0851|0.0510|
|Religion|-0.000044|0.000013|0.00038|
|Birth governorate|0.0383|0.0574|0.0267|
|Total sample|1497|1525|1718|
|Non-missing score sample|2543|2118|2096|
|<br>Share of non-missing scores|0.589|0.720|0.820|
|Sample after correction|3246|3975|3719|

_Source:_ SYPE 2009 survey. _Sample:_ 12-14 y.o. primary, 14-18 y.o. preparatory, and 17-21 y.o. secondary graduates.

We also perform the decomposition of the partial shares of scores inequality associated with the different circumstance variables. Birth governorates explain again significant shares of the inequality in exam scores: about 6 percent at the preparatory, which is rather consistent with the results obtained using TIMSS data. Family background explains the largest shares of exam scores variations. Parent’s educational attainments in particular explains, respectively, 12, 16 and 12 percent of the variation of scores at primary, preparatory, and secondary levels, respectively, while 26
family wealth explains 8, 9 and 5 percent at the corresponding levels. Gender explains a significant share of scores inequality only at the secondary exam (3 percent). Again religion has no explaining power. These results thus suggest that family resources and geography explain most of the variation in achievements at official exams.

# **4. Some Evidence on the Determinants of Learning Outcome Inequalities**

The findings of Section 3 showed that a significant share of achievement inequality is associated with circumstances determined at birth. They also suggest that these inequities do not appear suddenly at adolescence, such as at tracking taking place at the end of basic education, but rather build progressively through primary and lower secondary schooling grades. Here we look at some of the factors affecting learning inequalities, particularly at the early years. We focus on three main factors: type of school system attended, household spending on private tutorials, and tracking. Egypt has different school systems, such as public, private (including religious) schools, some of which use English or other languages for teaching. For our analyses, we use information from the SYPE survey on the type of the primary, preparatory and secondary schools attended by teenagers, distinguishing private, public, but also experimental language and traditional schools, and on households’ expenditures in private tutoring at the same levels.

Based on data from the UNESCO Institute of Statistics, **Figure 4** reports the evolution of public spending on education in Egypt, in share of GDP, and benchmarks it with the corresponding public spending in lower middle countries, Arab and Middle East and North Africa countries (note that the series of data are incomplete). It shows that public spending in education has stagnated over the long run (and decreased compared to the early 1980s) and slightly diminished in Egypt over a more recent period between 2003 and 2008, from about 5 to 3.9% of GDP. A similar evolution is observed in other MENA countries but the level of these expenditures is slightly lower in Egypt. The decline in public expenditure on education might contribute to the low learning achievement of many young Egyptians on the TIMSS test.

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<!-- Start of picture text --> Public spending on education, total (% of GDP)<br>65<br>6<br>+<br>5,5<br>° +<br>5=°<br>><br>45 a<br>a<br>4<br>35<br>3<br>25<br>2<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>=TM Egypt, Arab Rep. ==Lower middle income<br>Arab World == Middle East & North Africa (all income levels)<br><!-- End of picture text -->
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schools.

The pattern emerging from this description is that of large and increasing achievements gaps across schooling systems, with a divide between public regular and private or experimental schools at the primary and preparatory levels that is replaced by disparities between vocational and general schools at the secondary level.

If attending private schools allows children from advantaged backgrounds to perform better at the preparatory exam, allocation by school type, and attendance of a private school at the primary and preparatory levels, could contribute to educational inequities. However, rather than the school system, the differentials might just be explained by the selection of children with different characteristics into different school systems, such as the effects of other resources associated with a child’s background. To investigate this, **Table 10** reports the estimates of the equity-shares of learning achievements after controlling for the school system attended. The intuition underlying the analysis is that the controls should cancel out the indirect effects of a child’s circumstances through attendance of a different system. Thus comparing the share of achievements inequities in the uncontrolled and controlled analysis should inform on the differentials that stem from having benefited from a different system and associated schooling environment.<sup>15</sup>

> 15We rely here on OLS estimates of the effects of attending different school systems. An obvious limitation is that the estimates will be biased as soon as some unobservable variables that affect learning outcomes are associated with the school system attended.

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**Table 10: Opportunity Share of Inequality in Test and Exam Scores Controlling for the Allocation into Different School Systems**

||TIMSS Math test|Primary exam|Preparatory exam|Secondary exam|
|---|---|---|---|---|
|Base results|0.216|0.244|0.306|0.224|
|School system controls|0.211|0.240|0.303|0.180|
|School fixed effects|0.0718||||

Sample: SYPE 2009, samples of 12-14 y.o. primary, 14-18 y.o. preparatory, and 17-21 y.o. secondary graduates; source: SYPE 2009 survey.

The results suggest that, at the primary and preparatory levels, attendance of different school systems contributes very little to inequities in learning achievements. Taking the baseline uncorrected estimates as reference, and using data either from TIMSS test-scores or from the primary and preparatory completion exams in the SYPE survey, the share of achievements inequalities associated with a child’ circumstances remains virtually the same when controlling for attended school system. This suggests that, had they attended the same school systems, the gaps between pupils from different backgrounds would not have differed.<sup>16</sup> On the other hand, tracking into general and vocational secondary schools at the secondary level does explain a significant share of learning achievements inequities. Although circumstances continue to drive large learning differentials, controlling for school type does reduce significantly their effects (the estimated lower bound decreases from 22 to 18 percent). Hence, while a significant share (25 to 35 percent) of pupils from more privileged background attend private schools, we do not observe that this allocation explains a large share of achievement differentials at the primary and preparatory levels. However the tracking of children from different backgrounds into general and vocational schools does seem to contribute to achievements gaps at the secondary level.

> 16 Introducing schools fixed effects does reduce considerably the estimates of the share of unequal opportunities - to 0.0781 for the TIMSS test-score at completion of preparatory -, suggesting that a large part of those inequities is captured by the attendance of different schools. However, this decrease is difficult to interpret as it also includes the effects of living in different neighborhoods (and more generally geography) and related family characteristics.

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<!-- Start of picture text --> Tutoring expenditures<br>at primary, preparatory, secondary levels<br>Gender Household wealth<br>fo) fo}<br>fo} fo)<br>oO oO 248<br>° ©<br>oON fo)N<br>ue<br>117<br>8 8 gs 91<br>7 33 45 65 36 64 cn 131725 172435 245436 ce 66<br>fo} Lo)<br>Boys Girls lowest second middle fourth highest<br>Mother’s education Birth region<br>344 fo}<br>oO oOfo)<br>fo}<br>o<br>o 207<br>2 x<br>wu 13 13<br>Sg 94 - 84 8 79 73m 83 69<br>~ 4659 49 47 70 50 4004 4<br>182631 27 a 270i 28 at 328 303<br>fo} fo)<br>No schooling Primary | Prep-Second Vocsecond Post-second urban gov. lower urban lower rural upper urban upper rural frontier<br><!-- End of picture text -->
preparatory. More strikingly, families of the highest wealth quintile spend about ten times more than families in the bottom wealth quintile, and the same is true when comparing the expenditures of parents with a college education with those of uneducated parents. For instance tutoring expenditures reach 340 pounds for preparatory pupils whose mothers have a college degree. Expenditures in tutoring are also much higher for pupils born in urban governorates (210 pounds in average at the preparatory level) than in rural ones (35 pounds in rural Egypt).

Thus tutoring expenditures, which are large and strongly linked with a pupil’s background, are likely to contribute to learning achievement inequities. To investigate, we re-estimated the effects of pupils’ circumstance characteristics on differentials in learning achievements after controlling for families’ expenditures in tutoring. We use as control the logarithm of total household expenditures in tutoring divided by the number of enrolled children.<sup>19</sup>

**Table 11: Opportunity Share of Inequality in National Exam Scores Controlling for the Expenditures on Tutoring**

||**Primary**|**Preparatory**|
|---|---|---|
|Overall inequality|128.1|177.6|
|Base results|0.223|0.298|
|Tutoring household expenditures control|0.211|0.259|

_Source:_ SYPE 2009 survey. _Sample:_ 12-14 y.o. primary, 14-18 y.o. preparatory, and 17-21 y.o. secondary graduates.

**Table 11** reports the estimates of the equity-shares of learning achievements after controlling for the levels of household expenditures in tutoring at the primary and preparatory levels. These are obtained using the SYPE 2009 data. Taking the baseline uncorrected estimates as reference, the opportunity share of inequalities in achievements at the primary exam decreases only slightly from 22.3 to 21.1 percent when controlling for tutoring expenditures. But, even if it remains high, the corresponding share in achievements at the preparatory exam decreases much more

> 19 Of course, there are quite serious identification issues here. In particular, although averaging at the family level should mitigate this concern, tutoring expenditures are likely to depend on a child’s scholastic ability.

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drastically from 29.8 to 25.9 percent. Abstracting from the possible identification concerns, this suggests that, were parents to spend the same amounts in tutoring, gaps in achievements at the preparatory completion exam between children of different background would diminish by about 13 percent. Although a more causal analysis would be required, this descriptive finding suggests that differentials in tutoring expenditures do contribute to inequities in achievements at completion of basic education.

# **5. Conclusions**

This paper documents inequalities in educational achievements at basic and secondary education levels in Egypt and the extent to which these are associated with circumstances lying beyond the control of students, and investigates the contribution of potential institutional and behavioral factors to learning outcome inequalities.

Evidence from different sources of data suggests that, although basic education has been democratized, some inequities in access to general secondary and college education have persisted over the past two decades. The number of pupils tracked into vocational secondary schools has increased faster than those tracked to general secondary schools, and family background has become a more and more decisive factor in tracking.

Relying on recent methodological developments for the measurement of inequality of opportunity, the report estimates lower bounds for the opportunity shares of inequality in educational achievements. At least a quarter of inequalities in preparatory pupils’ TIMSS test scores can be attributed to pre-determined circumstances. With regard to national exam scores, the opportunity share of achievement inequalities is estimated to represent at least a third of overall inequalities at completion of preparatory schools and is already high, explaining at least a quarter of inequalities, at completion of primary. While these shares are intermediate in an international perspective, the high level of overall achievement inequality observed makes inequities in learning opportunities between Egyptian youth high compared to other countries in absolute levels.

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Moreover learning gaps among pupils from different backgrounds appear at early grades and are largely associated with geographic and family backgrounds.

These early inequalities in learning opportunities seem to result from both heterogeneous family cultural and financial resources and the institutions of the Egyptian educational system, with tracking reinforcing unequal family resources and early learning gaps. While finding no conclusive evidence that attendance of different school systems (public and private and experimental schools) at the primary and preparatory levels has significant effects, the results suggest that the high and unequal levels of household expenditures in private tutoring significantly contribute to achievement inequalities. Policy reforms to address inequity concerns therefore might need to deal not only with tracking and mobility in the educational system at upper levels, but also with learning gaps among younger pupils.

More causal analysis of the channels driving inequality in opportunities for learning achievements in Egypt is of course needed, as the empirical evidence remains scant. Future studies could focus on the effects of tracking of pupils at completion of preparatory school, deepen the analysis of school inputs (including within school systems) such as school management, and the effects of expenditures in tutoring and other private investments in education.

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