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Primary schooling in the household context

Iris Christiane GoenschCenter for international development and environmental research, University of Giessen

Senckenbergstr. 3,D-35390 [email protected]

The purpose of this paper is to explain the low level of school participation in Benin, Burkina

Faso, Mali, Niger and Senegal and to shed light on differences both between and within these countries.

In-depth descriptive analysis based on DHS data is followed by logistic regression analyses. Two

different measures of schooling are used. Interaction terms allow assessing if the household structure or

the place of residence have a different impact on girls' schooling than on boys' schooling. Results confirm

late enrollment and rural residence as main issues hampering complete enrollment and the completion of

four years of schooling. Except for Senegal, boys are significantly more likely to be enrolled in school

than girls. The negative impact of being female is statistically significant and of bigger magnitude for the

estimation of four years of schooling.

Keywords: school enrollment; West Africa; education; development; Millennium Development

Goals.

Acknowledgements: Funding from the Thyssen Foundation in support of the project

“Determinants of educational decisions" is gratefully acknowledged. Daniela Kautz and Steffen Graef

provided excellent research assistance.

1 Introduction

According to the UNDP Human Development Index, most West African countries

regularly rank among the last countries of the world. While their performance in all dimensions

of the index is rather poor and the countries are characterized as having low human

development, the educational dimension still is a specific weakness. Nevertheless, today most

young people have better access to education and receive longer schooling than their parents

and grandparents have had. As a result, school enrollment rates have risen especially at primary

school level during the last decades. This is also true for Benin, Burkina Faso, Mali, Niger and

Senegal, five francophone West African countries that are studied in this paper. Mean primary

school net enrollment ratios for 2001-2009 officially ranged from 54 percent (Niger) to 72.9

percent (Senegal) with Benin being a positive outlier with an enrollment ratio of 92.8 percent

1

(UNDP, 2010). Macro data thus points to a clear improvement of educational participation. On

the other hand, these aggregated figures do not provide any insights into the access that specific

groups have to educational facilities.

In this paper, I use data from the Demographic and Health Surveys (DHS) for Benin,

Burkina Faso, Mali, Niger and Senegal to study the determinants of primary school

participation, which is captured by two different outcome measures: Current school enrollment

of children of primary school age and the completion of four or more years of schooling of

young people aged 15 to 19.

Descriptive analysis reveals that enrollment is considerably higher in urban than in rural

areas for all five countries. Furthermore, there is a huge gap between male and female

enrollment: The share of boys enrolled is markedly higher for Benin, Burkina Faso, Mali and

Niger. As a first step in the regression analysis, I study the reasons for the differences in

enrollment and the generally low level using a logit approach. This allows assessing the

importance of child and family characteristics for explaining school enrollment. Interaction

terms give additional information on gender differences and allow to investigate if family

wealth, the household structure and urban or rural residence affect girls differently than boys.

The analysis of completion of four years of schooling is performed in a similar way.

The remainder of this paper is organized as follows: Section 2 briefly gives some general

information on West Africa and describes the state of schooling in Benin, Burkina Faso, Mali,

Niger and Senegal. The data sets and variables used are introduced in Section 3 which also

includes the descriptive and regression analysis on the individual level. Section 4 concludes.

2 Analysis of schooling in West Africa

Benin, Burkina Faso, Mali, Niger and Senegal belong to sub-Saharan Africa and are former

French colonies that gained independence in 1960. Table 1 gives some general information on

Table 1. Country characteristics.

Be

nin

Burkina Faso

Mali

Niger

Senegal

Total population (000)8,

66215

,23412

,70614

,70412

,211

Annual population growth rate (%)3.2

3.4

2.4

3.9

2.6

Population 0-14 years (%) 40 42 41 45 40Rural population (%) 59 80 68 83 58Life expectancy at birth (years) 61 53 48 51 56Poverty (% of pop. on less than $2 a

day) 75 81 77 86 60GDP per capita (PPP) US$ 14 11 11 68 17

2

76 68 46 4 96

HDI rank*134

161

160

167

144

Note: * is taken from Human Development Report 2010, all other figures are from UNESCO Institute for Statistics. Poverty rates are from 2003 for Benin and Burkina Faso, from 2006 for Mali, from 2007 for Niger and from 2005 for Senegal. If not otherwise indicated, all remaining data are from 2009.

the countries. A remnant common to these five countries, besides of French being the

official language, is a schooling system that is structured as the traditional French system: Six

years of primary schooling are followed by two cycles of secondary schooling: The first cycle

lasts four years, the second cycle, the lycée lasts three years. The transition from each stage of

schooling to the next one is limited by standardized exams. Only those students that pass the

exams are allowed to proceed with the first or second cycle of secondary school, respectively.

The baccalauréat exams at the end of the second cycle of secondary school entitle students to

enter higher education.

2.1 Progress in schooling

The eight Millennium Development Goals (MDGs) adopted at the UN Millennium

Summit in 2000 were set to be achieved by 2015. They cover a wide range of issues - poverty,

hunger, health issues, environmental sustainability, and gender equality - and also include

universal primary education. More precisely, the target is to “ensure that, by 2015, children

everywhere, boys and girls alike, will be able to complete a full course of primary schooling". 1

Three indicators have been adopted for monitoring progress toward this goal: The net

enrollment ratio in primary education, the survival rate at the last grade of primary school and

the literacy rate of 15 to 24 year-olds.

The development of enrollment rates in most regions of the world is promising, but

Africa is lacking behind.

Table 2. Education information.

Benin

Burkina Faso

Mali

Niger

Senegal

Public expenditure on education (% of GDP)

3.5 (2007)

4.6

3.8

3.7

5.1

NER % primary school (2008) 9360

72 49

73

NER % primary school male (2008) 9964

78 55

72

NER % primary school female (2008) 8656

65 43

74

Survival rate to grade 5 (%)70

(2003)82

87 69

70

1 http://www.unmillenniumproject.org/goals/gti.htm/3

Mean years of schooling (adults)* 3.51.3

1.4

1.4

3.5

Note: * Taken from UNDP (2010). All remaining figures are taken from UIS UNESCO Institute for Statistics (2010).

The educational expansion that took place in the middle of the last century was followed

by a long period of stagnation and even decline in some countries. In the course of the

Education for All (EFA) initiative, first launched in Jomtien, Thailand in 1990, basic education

has more and more become the focus of attention. Policies like school fee abolishment and an

expansion of the education sector have raised enrollment rates.

Table 2 includes the mean years of schooling of adults, which is very low especially for

Burkina Faso (at 1.3), Mali and Niger (at 1.4). Today's primary school enrollment rates point to

an improvement of these figures. It is, however, questionable, if the pace of the improvement

suffices to reach the millennium development goals. Lewin (2008) explores the current status of

education in relation to the Millennium Development Goals and the Education for All goals.

He stresses that high drop-out rates are a major threat to achieving the goals in sub-

Saharan Africa.

Similarly, Lloyd and Hewett (2003) investigate impediments to universal schooling in

sub-Saharan Africa. Their analysis based on DHS data shows that the gap between primary

enrollment rates of girls and boys is narrowing, which is partly due to a stagnation in boys'

attainment since the early 1980s. Specifically for francophone West Africa, the gap has

narrowed, but remains bigger than in other parts of the continent. In contrast to the gender gap,

the gap between the richest and the poorest households is still huge. The very low school

participation of the two poorest quintiles is a major impediment, according to the authors.

Bennell (2002) focuses on the target of doubling primary school enrollments in sub-

Saharan Africa by 2015 and points out that it will be difficult to provide enough facilities to

increase the capacities of primary schooling. The rapid expansion of the sector has already led

to quality problems that are reflected by high repetition rates. In most sub-Saharan African

countries, completion rates remained unchanged or even declined during the second part of the

1990s. These quality problems and low chances of school completion lower (perceived) rates of

returns to education. While for the last generation already completed primary education had

significantly improved job opportunities, educational expansion has led to higher requirements.

The fact that the availability of secondary schools is still limited in many (rural) areas

contributes to the issue of declining perceived returns to education and diminishes demand for

primary schooling.

These consequences have for example been encountered in Malawi, where the

government has started its policy of free primary education in 1994. While this program could

theoretically have lead to the attainment of the MDGs, Kadzamira and Rose (2003) find that the

4

affect of the policy is ambiguous and not pro-poor. Even though school fees and uniforms have

been abolished, costs for families with many children remain high: Direct costs include school

materials and indirect costs are foregone income and time to care for family members,

especially younger siblings or AIDS patients. High repetition rates lead to a long time of

primary schooling which increases these costs. The increase of enrollment provoked by the

abolition of fees made the employment of additional teachers necessary so the share of qualified

teachers dropped. Since programs for the education of adults are insufficient, the age range at

primary schools is very wide (four to 18 years) which complicates teaching. The channeling of

resources to primary schooling also means that investments into secondary schooling were

reduced so hat problems at the transition to secondary schooling aggravated. School curricula,

however, are oriented on those students who attend secondary school, so that the relevance of

schooling might be not so clear for children that will only attend for a few years. According to

Kadzamira and Rose (2003), the quality problems disproportionably affect the rural poor who

are confronted with bigger class sizes and less qualified teachers.

2.2 Parental income and other influence factors

Glick and Sahn (1999) analyze determinants of several schooling indicators in Conakry,

the capital and main urban center of Guinea, and gender differences therein. They find that

siblings under five living in the household have a negative impact on the enrollment of girls and

that increases in household income improve girls' schooling but have no significant impact on

the schooling indicators for boys. Furthermore, mothers' education is found to be positively and

significantly related to girls' schooling only.

Lewin and Sabates (2011) find that primary school participation is still very much

determined by household wealth in spite of Education for All and commitments to pro-poor

policies. Their analysis based on data from two DHS waves also shows that girls and children

living in rural areas are more likely to be out of school than boys and urban residents in most of

the seven francophone sub-Saharan African countries considered. Overall, they find lower

levels of schooling in the francophone countries and less progress between the two survey

waves than in the anglophone countries. The share of pupils that are two or more years older

than the age officially corresponding to the grade, however, is clearly higher in anglophone

countries.

Grimm (2011) uses a natural experiment in Burkina Faso to explore the effect of

household income on the investments in children's education. Results indicate a high income

elasticity of school enrollment. The decrease in enrollment as a consequence of lower income is

bigger for girls than for boys. In settings where the (rural) population relies on few income

5

sources that heavily depend on favorable external conditions such as weather and world

markets, income shocks and reduced purchasing power are hard to cushion.

Montgomery and Hewett (2005) use data from the Senegal Multiple Indicator Cluster

Survey 2000 to analyze schooling in urban and rural Senegal. They calculate a measure of

relative standard of living for each household and find a high impact of living standards on three

measures of schooling especially in urban areas. In addition to current enrollment, Montgomery

and Hewett consider whether a young person has ever attended school and whether she or he

has completed four or more grades of primary school. For all of these measures, they find lower

school participation of rural residents and of girls, but the gender gap is smaller for enrollment

and almost negligible for the enrollment of girls from specific age groups.

2.3 The importance of the demand side

Most household surveys do not provide information on the availability and accessibility

of facilities such as (primary) schools. A common criticism to studies that analyze schooling

with these data is that the most important determinant of enrollment is school availability which

is not captured in these studies. Filmer (2007) combines household data with official data on

school availability to assess the impact of building new schools, i.e. reducing the distance to the

nearest school, on enrollment. For his sample of rural areas in 21 low-income countries, he finds

that distance to school and enrollment are negatively associated, but that the magnitude of the

effect is small. As consequence, the study suggests that building schools might not be the best

way to increase enrollment and that the demand side plays an important role.

Similarly, Handa (2002) finds that school availability has a positive and significant

impact on enrollment in rural Mozambique. Regarding the demand side, literacy programs for

household heads also seem to sharply increase the demand for primary schooling for children

while the effect of increasing income levels is modest. Considering cost-effectiveness of

interventions, the author identifies increasing adult literacy as a measure that is at least as

efficient as increasing school coverage.

3 Empirical analysis of schooling in the household context

3.1 Data and descriptive statistics

Household data from the Demographic and Health Surveys (DHS) are available for the

five countries for adjacent years. The surveys in Benin, Mali, Niger and Senegal were all

conducted in 2005 or 2006, only the survey in Burkina Faso took place earlier, in 2003. DHS

belong to the most widespread multipurpose-surveys carried out in developing countries and are

6

representative on the national as well as regional level. The datasets provide information

regarding the composition of households, socio-economic data for all household members,

information on housing, agricultural activities, assets and much more. While the questionnaires

are standardized, not all questions are always asked in all countries.2

I use two different measures of schooling that are related to the millennium development

goal of universal primary education. The United Nations have established three indicators to

monitor progress towards this goal: Firstly, the net enrolment ratio in primary education,

secondly, the proportion of pupils starting grade one who reach the last grade of primary school

and thirdly, the literacy rate of 15- to 24-year-olds.3 These indicators take into account that

primary schooling has a different duration in different countries. If the first two of them reach

levels of 99 percent, universal primary schooling is tangible. If this was the case, complete

literacy of young adults would be achieved. All these indicators show if the goal is reached for

several cohorts of pupils.

While the information on literacy in the DHS data is not sufficient, the first two indicators

are reflected in the data and used in this paper. Current enrollment is analyzed for children that

were of official primary school age at the time of the survey, that is children aged six to eleven

for Benin and Burkina Faso and children aged seven to twelve for Mali, Niger and Senegal.

Whether a young person has completed four or more years of schooling is the second binary

outcome variable.4 People that have attended school for four or more years are very likely to

have acquired basic literacy and numeracy that they will not quickly forget. Children who enter

primary school at the age of six or seven can complete four years of schooling at the age of ten

or eleven. Because of widespread late enrollment, I will consider youths aged 15 to 19 for the

analysis of completion of four years of schooling.

Table 3 shows the enrollment rates and the means of all explanatory variables calculated

from DHS data for children of official primary school age. Enrollment in Benin (61.7 %) is

slightly higher than in Senegal (58.2 %) and substantially higher than in the other countries. The

lowest enrollment rate is found for Burkina Faso where only 27.7 % of primary school aged

children are enrolled.

2 For detailed information on and access to the datasets, see http://measuredhs.com. 3 http://mdgs.un.org/unsd/mdg/Host.aspx?Content=Indicators/O_cialList.htm

4 For this paper, this does not mean that a person has reached grade five, but only that it has attended school for at least four years - irrespective of the grade. Therefore I should find a slightly higher rate than the UNESCO Institute for Statistics that uses the percentage of a cohort of pupils enrolled in grade one in a given year who are expected to reach the last grade of primary school, regardless of repetition. Furthermore, drop-outs who return to school are included in my measure but not in the UNESCO measure. However, the tendency of this measure should be the same.

7

http://mdgs.un.org/unsd/mdg/Host.aspx?Content=Indicators/O_cialList.htm

http://measuredhs.com/

For all countries, the calculated enrollment rates are lower than the official ones shown

by Table 2, basically for the following reasons: Official data is often based on supply-side data,

i.e. the number of pupils that schools report to the authorities; and schools often have incentives

to report high numbers. Even if children drop out of school early during the school year or never

turn up, they are still officially considered to be enrolled once they were registered in the

beginning of the year. In contrast, during the Demographic and Health Surveys, the respondent

is asked, if a child is currently frequenting school. Moreover, net enrollment is defined as the

number of official primary school age children who are enrolled in primary education as a

percentage of the total children of the same age group. The official enrollment numbers are thus

divided by estimates of the population of the age group. These estimations, however, are not

always accurate and are subject to changes in underlying management information systems.5 On

top of this, Table 2 shows figures for 2008, while the DHS data used in this paper, were

collected between 2003 and 2006.

Figure 1. Enrollment by age.

Source: Author’s calculations using DHS data.

Figure 1 shows that enrollment rates are highest for children aged nine to eleven and

decline for older children. This pattern exists within all five countries and the ranking of the 5 See Lloyd and Hewett (2003) for these and other concerns about the UNESCO net primary

school enrollment ratios.

8

countries for almost every child age reflects the overall enrollment rates. The fact that maximal

enrollment rates are not reached at the official school entry age, but at least three years later,

illustrates the problem of late enrollment. The importance of the place of residence is illustrated

by Figure 2: Urban enrollment is markedly higher than rural enrollment and the gap is

particularly large in Burkina Faso and also in Niger. Keeping in mind that urbanization is very

low, the main overall differences in the enrollment rates of the countries stem from differences

in rural areas. completion of four years of schooling, but the age group included will be

different. In this table, only children of official primary school age are included. On the

individual level, age and

Figure 2. Enrollment by place of residence for children of official primary school age.

Source: Author’s calculations using DHS data.

gender of the child (dummy female) are included. The highest education of an adult male

household member is included in four categories: Incomplete primary schooling, complete

primary schooling, secondary schooling or higher schooling and the reference category no

education. Variables for the schooling of an adult female household member are included in the

same way. Since the data sets also include some households without adult male or female

household member respectively, dummy variables for these cases are included. Wealth quintiles

derived from an asset index indicate to which quintile of a country a household belongs.

1. wealth quintile thus indicates that a child lives in a family that is part of the poorest fifth of

the country according to the asset index. In the estimations, the lowest wealth quintile serves as

reference group.

9

A dummy variable for urban residence is also included. Since a higher number of

household members could both mean a more tightened resource rivalry, more household chores,

but also less household work for a particular child, in addition to the number of household

members, I include variables that refer to the structure of the household. Particularly, five

variables indicate the number of children of different age groups and sex.

At first glimpse, the higher urbanization in Benin and Senegal as well as the high number

of household members (# household members) in Burkina Faso and Senegal attracts one's

attention. Unfortunately, the big household size in Senegal might partly be driven by few

outliers. Altogether, the descriptive statistics show that adult education levels - as enrollment -

Table 3. Descriptive statistics: Means.

Variable Benin Burkina Faso Mali Niger Seneg

al Enrolled 0.617 0.277 0.433 0.376 0.582Age 8.278 8.279 9.326 9.276 9.407Female 0.479 0.487 0.496 0.492 0.502 highest education of a male adult hh memberincomplete

primary 0.175 0.076 0.100 0.091 0.153complete

primary 0.031 0.063 0.022 0.015 0.044secondary + 0.221 0.092 0.127 0.071 0.224no adult males

in hh 0.13 0.036 0.072 0.125 0.074 highest education of a female adult hh memberincomplete

primary 0.137 0.055 0.090 0.094 0.206complete

primary 0.017 0.049 0.015 0.012 0.034secondary + 0.101 0.056 0.072 0.047 0.156no adult

females in hh 0.016 0.007 0.010 0.008 0.011 1. wealth

quintile 0.216 0.202 0.217 0.223 0.2292. wealth

quintile 0.217 0.200 0.211 0.206 0.2273. wealth

quintile 0.210 0.269 0.208 0.203 0.2104. wealth

quintile 0.191 0.172 0.199 0.190 0.1795. wealth

quintile 0.166 0.158 0.163 0.178 0.155 urban 0.338 0.134 0.265 0.153 0.371# household

members 7.808 10.03 8.125 8.63113.38

6 # children < 6 1.603 1.964 1.630 1.903 2.588

10

years# sisters aged 6-

12 0.822 0.994 0.821 0.936 1.182#brothers aged

6-12 0.919 1.091 0.925 1.005 1.572# sisters aged

13-20 0.509 0.746 0.586 0.523 1.182# brothers aged

13-20 0.622 0.959 0.742 0.618 1.222

# observations1735

01171

91309

8 95021156

7Source: Author's calculations using DHS data. Sample weights are applied. Children of official

primary school age are considered.

are lower in Burkina Faso, Mali and Niger than in Benin and Senegal which are also

more urbanized countries.

Table 4 underlines the fact that school enrollment for children of official primary school

age is far from complete for both sexes and even for the richest wealth quintile where school

participation rates are nevertheless higher. Only the enrollment level of 93.38 percent for boys

of the richest quintile in Benin shows that complete enrollment might be tangible for this group.

Leaving Senegal aside for a moment, it is easy to see that boys' enrollment is always

higher than girls' enrollment. Both for girls and for boys, belonging to a higher wealth quintile is

related to higher enrollment rates. Interestingly, for girls, the biggest gaps can be found between

the fourth

Table 4. Enrollment rates by sex and wealth quintile (in %).

BeninBurkina

Faso Mali Niger Senegal

B

oysG

irlsB

oysG

irlsB

oysG

irlsB

oysG

irlsB

oysG

irlsF

irst4

4.303

2.741

4.991

0.893

4.412

6.543

0.892

1.394

2.154

2.08S

econd5

5.094

7.712

0.561

4.803

8.153

1.443

6.712

0.495

1.795

0.94T

hird6

6.245

9.912

4.592

1.053

7.613

2.263

9.562

3.845

4.615

8.41F

ourth7

8.007

1.673

4.372

1.584

9.654

2.224

4.792

7.716

8.967

0.15F

ifth9

3.388

0.986

6.746

5.628

2.707

5.027

4.086

5.838

1.748

0.52T

otal6

5.375

7.632

9.942

5.244

6.574

0.054

4.103

0.855

7.395

9.01Note: Children of official primary school age of each country are included, i.e. children aged six to

eleven for Benin and Burkina Faso and children aged seven to twelve for Mali, Niger and Senegal. Sample weights are applied.

and fifth wealth quintiles for Burkina Faso, Mali and Niger. For Benin and Senegal, the

biggest gap of girls' enrollment is between the third and fourth quintile. Thus, belonging to the

richest wealth quintile (or to one of the two richest quintiles for the two countries with higher

11

school participation rates) establishes an exceptional position for girls, while for example

enrollment differences between the first and second wealth quintile are rather modest (with the

exception of Benin). For Senegal, the enrollment rates for girls and boys do not differ much. As

in official data, they even tend to be higher for girls which seems to be driven by the third and

fourth wealth quintiles. Burkina Faso has one of the lowest school participation rates in the

world, but the big difference to Niger might partly stem from the fact that the Burkinabé data

have been collected three years earlier.

Wealth, urban or rural residence, sex and child age have thus been identified as key

factors when analyzing differences in school enrollment.

Table 5. Completion of four years of schooling (in percent).

Benin Burkina Mali Niger SenegalA

geB

oysG

irlsB

oysG

irlsB

oysG

irlsB

oysG

irlsB

oysG

irls1

04

3.053

1.671

3.971

4.581

1.637

.417

.785

.561

0.509

.861

16

3.995

1.602

8.612

3.802

2.331

9.222

4.731

7.613

2.383

0.081

26

5.755

3.473

3.682

3.053

8.752

7.903

5.032

4.224

0.044

0.581

37

2.325

7.693

4.612

9.504

3.193

5.544

5.662

3.964

6.564

0.101

47

5.186

0.713

4.943

0.555

0.393

9.953

8.682

8.835

4.924

9.691

56

9.505

8.043

4.552

6.684

5.753

4.693

4.012

6.115

0.244

0.431

67

7.446

1.454

0.322

7.895

2.503

7.443

9.542

0.705

4.284

6.251

77

1.315

3.003

5.032

8.894

7.183

0.923

4.121

8.695

8.404

3.951

86

8.604

5.003

6.082

6.254

0.532

9.633

8.511

7.484

9.353

6.891

97

8.114

9.523

4.322

9.203

8.763

1.394

3.032

2.095

5.454

0.182

06

3.883

0.853

2.101

9.233

7.291

7.912

3.871

3.644

1.423

1.141

0-206

6.634

9.183

1.972

5.093

7.732

7.493

0.471

8.824

4.043

7.211

5-19 63.50 32.08 39.30 28.30 47.10

Source: Author's calculations using DHS data. Sample weights are applied.

Table 5 introduces the second outcome measure, the completion of four years of

schooling. Children that start school at age six or seven should theoretically finish four years of

schooling at age ten or eleven. The table shows, however, that late enrollment or school

interruptions cause delays. This is why the age group 15-19 is chosen for the estimation of four

years of schooling. Further widening the age group to the top would cause a loss in actuality and

12

widening it to younger ages would exclude young people who are still enrolled at school and

have not yet finished four years of schooling.

Figure 3 shows the share of the population aged five to 65 that have ever attended school.

The highest share is reached for the cohorts that are ten to twelve years old, where (except in

Burkina Faso) more than half of these cohorts have

Figure 3. Share of people that have ever attended school.

Source: Author's calculations using DHS data.

been to school. On the one hand, the figure reflects the problem of late enrollment, but on

the other hand it also illustrates that much progress has been achieved considering the long run.

The pattern of completion of four years of schooling looks quite different: As illustrated

by Figure 4, the highest proportions are reached between age 14 and age 16.

13

Figure 4. Completion of four years of schooling (proportion).

Source: Author's calculations using DHS data.

3.2 Regression analysis

A logistic regression is conducted to analyze current school enrollment. In addition to the

explanatory variables presented above, interaction terms are introduced into the estimation.

female is interacted with all variables that indicate the structure of the household, particular the

number of small children and boys and girls of different age groups as well as with the wealth

index variables and the dummy for urban residence. These interaction terms will allow

revealing different effects of these variables on girls' enrollment than on boys' enrollment.

Regarding the regression results presented in Table 6, the coefficient of age is positive

and strongly significant for all five countries. Interestingly, even incomplete primary schooling

of a female or male household member is positively and significantly related to a child's

enrollment.

Still, the effect of secondary or higher schooling - especially of men - is stronger. The

impact of female is not straightforward because of the interaction terms included in this non-

linear regression.6

6 See e.g. Ai and Norton (2003) for detailed information on interaction terms in logit and probit models.

14

Therefore, the unconditional average marginal effects of being female and the marginal

effect conditional on living in an urban or rural area7 have been calculated. Table 7 shows that

boys

Table 6. Logit regression results: School enrolment (children of official primary school

age)

Beni

nBur

kina MaliNige

rSene

gal

age2.09

70***3.24

29***1.24

70***0.81

99***1.64

47***

(0.1

25)(0.1

80)(0.1

50)(0.1

76)(0.1

59)

age # age-

0.1069***-

0.1714***-

0.0596***-

0.0424***-

0.0832***

(0.0

07)(0.0

11)(0.0

08)(0.0

09)(0.0

08)

female-

0.6200***-

0.4758***-

0.3972***-

0.4936***-

0.0448

(0.0

86)(0.1

41)(0.1

13)(0.1

25)(0.1

06) highest education of a male adult hh member

incomplete primary0.62

83***0.56

38***0.60

49***0.53

06***0.85

76***

(0.0

49)(0.0

84)(0.0

63)(0.0

78)(0.0

62)

complete primary0.85

20***0.89

68***0.79

21***0.34

10**1.00

54***

(0.1

14)(0.0

90)(0.1

31)(0.1

57)(0.1

22)

secondary +1.05

00***1.15

23***1.28

86***1.41

16***1.36

65***

(0.0

58)(0.0

90)(0.0

71)(0.1

07)(0.0

75)

no adult males in hh0.50

64***0.01

26-

0.01370.30

42***0.34

24***

(0.0

59)(0.1

22)(0.0

79)(0.0

74)(0.0

83) highest education of a female adult hh member

incomplete primary0.49

08***0.50

43***0.55

10***0.54

02***0.80

22***

(0.0

59)(0.0

92)(0.0

70)(0.0

79)(0.0

58)


54***0.88

13***1.02

15***0.57

32***1.03

28***

(0.1

73)(0.1

01)(0.1

68)(0.2

20)(0.1

46)

secondary +0.65

92***1.03

25***1.14

76***1.05

11***1.47

77***

(0.0

88)(0.1

16)(0.1

08)(0.1

23)(0.0

93)

no adult females in hh0.06

910.04

390.23

45-

0.6415***-

0.6686*** (0.1 (0.2 (0.1 (0.2 (0.2

7 The table is not included in this paper, but available upon request.15

52) 84) 81) 44) 35)wealth quintile

second quintile0.39

18***0.39

18***0.00

960.33

86***0.27

51***

(0.0

65)(0.1

13)(0.0

83)(0.1

02)(0.0

80)

third quintile0.85

58***0.65

62***0.11

340.38

94***0.24

34***

(0.0

69)(0.1

04)(0.0

82)(0.1

03)(0.0

88)

fourth quintile1.25

58***1.05

35***0.43

73***0.54

60***0.11

04

(0.0

80)(0.1

11)(0.0

87)(0.1

01)(0.1

17)

fifth quintile2.13

95***1.47

21***1.36

57***1.26

87***0.09

29

(0.1

28)(0.1

38)(0.1

30)(0.1

34)(0.1

46)

urban0.14

18**0.82

82***0.39

99***0.28

52**0.29

56***

(0.0

57)(0.1

11)(0.0

79)(0.1

12)(0.0

85)

# household members-

0.0801***-

0.0134-

0.0522***-

0.0451***-

0.0283***

(0.0

14)(0.0

13)(0.0

17)(0.0

17)(0.0

10)

# children under 60.01

55-

0.0691**0.05

86**0.04

29-

0.0325

(0.0

25)(0.0

30)(0.0

29)(0.0

31)(0.0

21)

# sisters aged 6-120.03

70-

0.03470.05

750.07

92**0.04

93

(0.0

31)(0.0

36)(0.0

36)(0.0

39)(0.0

30)

# brothers aged 6-12-

0.0157-

0.0903***-

0.0029-

0.0313-

0.0157

(0.0

28)(0.0

33)(0.0

32)(0.0

36)(0.0

24)


930.01

650.03

780.17

44***-

0.0323

(0.0

38)(0.0

40)(0.0

41)(0.0

47)(0.0

29)


0.0378-

0.1586***-

0.03460.00

88-

0.0398

(0.0

33)(0.0

34)(0.0

34)(0.0

43)(0.0

27)Note: The table is continued on the next page

Logit regression results: School enrollment, continued

Beni

nBur

kina MaliNige

rSene

gal

female # children <60.03

34-

0.0101-

0.0545*0.01

66-

0.0266

(0.0

25)(0.0

35)(0.0

31)(0.0

32)(0.0

23)female # sisters aged 6-

12-

0.0072-

0.01420.03

58-

0.00460.00

93

(0.0

37)(0.0

46)(0.0

44)(0.0

50)(0.0

39)

16

female # brothers aged 6-12

0.0706**

0.0833*

0.0476

0.0557

0.1103***

(0.0

35)(0.0

45)(0.0

40)(0.0

45)(0.0

31)female # sisters aged

13-20-

0.0660-

0.04780.00

92-

0.0257-

0.0788**

(0.0

48)(0.0

52)(0.0

51)(0.0

61)(0.0

37)female # brothers aged

13-200.08

32**0.08

40*0.03

06-

0.05100.06

42*

(0.0

40)(0.0

43)(0.0

39)(0.0

53)(0.0

36)female # 2. wealth

quintile0.19

57**0.01

380.14

96-

0.3194**-

0.0953

(0.0

98)(0.1

74)(0.1

22)(0.1

55)(0.1


quintile0.18

38*0.07

190.16

01-

0.2221-

0.0152

(0.1

02)(0.1

60)(0.1

19)(0.1

54)(0.1


quintile0.07

14-

0.3083*0.10

50-

0.3508**-

0.1440

(0.1

14)(0.1

72)(0.1

26)(0.1

52)(0.1


quintile-

0.7877***-

0.1139-

0.3302*-

0.4081**-

0.0501

(0.1

62)(0.2

03)(0.1

79)(0.1

86)(0.1

97)

female # urban0.00

730.08

520.07

620.54

64***0.17

13

(0.0

82)(0.1

61)(0.1

12)(0.1

54)(0.1

20)

Constant-

9.7293***-

16.2066***-

6.8759***-

4.6661***-

7.9941***

(0.5

13)(0.7

63)(0.7

00)(0.8

15)(0.7

40)

Observations17350

11702

13098

9498

11373

Pseudo R-squared0.18

180.23

170.15

160.17

630.15

22Note: Robust standard errors in parentheses; *** p <0.01, ** p<0.05, * p<0.1

are more likely than girls to be enrolled in all countries except Senegal. The enrollment

probability of girls is six percent (Burkina Faso and Mali), eight percent (Benin) and eleven

percent (Niger) lower, respectively. For Senegal, the effect of being a girl on enrollment is

slightly positive and statistically insignificant. Testing equality of the conditional marginal

Table 7. Average marginal effects of being female.

Delta-methodCount

rydy/

dxp-

Value

Benin-

0.081 0.000

17

Burkina

-0.063 0.000

Mali-

0.061 0.000

Niger-

0.114 0.000Seneg

al0.00

3 0.759Note: dy/dx for factor level is the discrete change from the base level

enrollment prospects by 14.6 percent, while the effect of being a girl in an urban area is

much smaller (-3.6 percent) and not statistically significant. Marginal effects also indicate that

effects of being female reveals that the different impact of rural or urban residence is only

statistically significant for girls' enrollment in Niger: There, being a girl in rural areas lowers

household wealth seems to play a less important role in Senegal than in the other countries

where belonging to a higher wealth quintile is related to better education prospects. Living in a

bigger household is associated with lower enrollment in all countries. The average marginal

effect of

Table 8. Logit regression results on completion of four or more years of schooling.

BeninBurki

na Mali NigerSeneg

al

age1.092

2*0.206

02.157

8***-

1.51461.148

4*

(0.63

3)(0.68

4)(0.66

6)(0.97

4)(0.62

0)

age # age-

0.0400**-

0.0099-

0.0709***0.040

2-

0.0403**

(0.01

9)(0.02

0)(0.02

0)(0.02

9)(0.01

8)

female-

1.3535***-

0.8329***-

0.8882***-

0.8210**-

1.0924***

(0.16

2)(0.20

1)(0.17

9)(0.32

8)(0.17

5) highest education of a male adult hh member incomplete

primary0.805

1***0.898

8***1.066

8***0.914

1***1.085

7***

(0.09

1)(0.10

5)(0.10

1)(0.13

0)(0.08

3)


7***1.445

4***1.157

3***1.905

9***1.330

1***

(0.17

0)(0.11

4)(0.17

5)(0.26

7)(0.15

1)

secondary +1.960

0***1.768

3***1.722

3***1.901

3***1.987

5***

(0.08

5)(0.10

6)(0.08

9)(0.14

1)(0.08

6)no adult males in

hh1.109

9***0.570

8***0.357

3***0.625

3***0.738

3*** (0.10 (0.16 (0.13 (0.16 (0.11

18

9) 8) 4) 5) 4) highest education of a female adult hh member incomplete

primary0.822

1***0.803

1***0.961

5***1.134

8***0.899

6***

(0.10

9)(0.13

4)(0.11

0)(0.14

2)(0.07

7)


6***1.674

8***1.271

0***1.416

6***1.452

4***

(0.28

3)(0.16

2)(0.23

3)(0.36

1)(0.17

1)

secondary +2.115

0***1.257

1***1.790

3***1.606

8***2.019

3***

(0.13

3)(0.14

1)(0.12

3)(0.17

0)(0.10

2)no adult females

in hh0.148

8-

0.3614**-

0.3723**-

0.4978**-

0.4508**

(0.12

9)(0.17

8)(0.14

5)(0.23

0)(0.21

2)wealth quintile

second quintile0.436

5***0.185

10.144

0-

0.02480.184

2

(0.11

8)(0.15

1)(0.12

7)(0.26

3)(0.12

8)

third quintile0.600

1***0.405

1***0.331

2***0.232

60.162

7

(0.12

1)(0.14

2)(0.12

3)(0.23

7)(0.13

7)

fourth quintile0.912

8***0.555

3***0.508

7***0.357

00.173

7

(0.13

6)(0.15

3)(0.13

3)(0.24

0)(0.16

3)

fifth quintile1.054

7***0.812

8***1.036

8***0.992

3***-

0.0585

(0.16

8)(0.18

8)(0.18

0)(0.27

0)(0.18

0)

urban-

0.01120.868

8***0.175

20.430

1**0.403

8***

(0.09

7)(0.15

1)(0.11

5)(0.19

0)(0.10

8)# household

members-

0.0751***-

0.0758***-

0.0463**-

0.0650**-

0.0637***

(0.02

2)(0.01

8)(0.02

3)(0.02

7)(0.01

4)# children under

6-

0.05290.076

6*0.040

10.021

0-

0.0330

(0.04

3)(0.04

1)(0.04

3)(0.05

4)(0.03

3)# sisters aged 6-

120.145

7***0.015

50.050

60.213

2***0.062

4

(0.05

0)(0.04

6)(0.05

0)(0.07

0)(0.04

3)Note: The table is continued on the next page

Logit regression results on completion of four or more years of schooling, continued

BeninBurki

na Mali NigerSeneg

al

# brothers aged 6-120.010

50.106

3**0.023

10.014

30.089

8***19

(0.04

7)(0.04

5)(0.04

5)(0.06

4)(0.03

5)


1*0.070

50.140

5**0.095

70.040

2

(0.06

1)(0.05

7)(0.05

9)(0.08

6)(0.03

9)


0.0551-

0.0136-

0.03010.074

9-

0.0298

(0.04

9)(0.04

3)(0.04

4)(0.07

5)(0.03

4)

female # children <6-

0.0965*-

0.0937*-

0.0754-

0.07820.019

0

(0.05

3)(0.05

1)(0.05

2)(0.07

0)(0.03

6)female # sisters aged 6-

12-

0.08420.095

30.249

6***-

0.14410.025

5

(0.06

6)(0.06

7)(0.06

8)(0.09

7)(0.05


6-120.199

8***0.038

20.132

8**0.143

00.077

0

(0.06

3)(0.06

2)(0.06

3)(0.09

3)(0.04

8)female # sisters aged

13-20-

0.03270.061

10.040

4-

0.0585-

0.0215

(0.08

4)(0.07

6)(0.07

7)(0.10

9)(0.04


13-200.115

4*0.065

00.047

7-

0.03280.031

5

(0.06

4)(0.05

9)(0.06

4)(0.10

2)(0.04


quintile0.018

2-

0.29140.018

20.092

40.262

2

(0.18

8)(0.26

1)(0.21

1)(0.41

9)(0.19


quintile-

0.05850.045

1-

0.2252-

0.34710.628

4***

(0.19

2)(0.23

4)(0.20

4)(0.40

7)(0.19


quintile0.068

7-

0.0952-

0.09540.195

90.487

4**

(0.20

5)(0.24

8)(0.21

1)(0.38

6)(0.23


quintile-

0.5626**-

0.4213-

1.1037***-

0.37780.662

2***

(0.23

6)(0.29

5)(0.25

8)(0.42

7)(0.25

3)

female # urban0.161

20.107

60.227

30.866

0***-

0.2089

(0.14

3)(0.22

7)(0.16

9)(0.28

8)(0.15

1)

Constant-

7.3657-

2.0687-

17.3523***12.23

60-

8.7735*

(5.32

9)(5.76

0)(5.58

5)(8.16

4)(5.21

9) Observations 6950 5994 6410 3615 7038

Pseudo R-squared0.294

10.287

10.260

70.371

30.281

4

20

Note: All young people aged 15 to 19 are included in this regression. Robust standard errors in parentheses. *** p <0.01, ** p<0.05, * p<0.1.

urban residence on enrollment is statistically significant and positive for all countries:

Urban residents of primary school age are 2.7 percent more likely to be enrolled in Benin and

even 15 percent more likely in Burkina Faso.

No clear pattern emerges regarding the effect of the presence of (younger) children in the

household: The presence of children under age six has a statistically significant and negative

effect on schooling in Burkina Faso and Senegal, but the magnitude of the effect is small. Also

for Burkina Faso, the presence of older boys (aged 13 to 20) has a negative effect on schooling,

especially on enrollment of boys. Possibly, education investments are channeled to the oldest

son. But no evidence for this could be found for the other countries analyzed here. For both

sexes, belonging to a higher wealth quintile is associated with higher enrollment prospects. In

Burkina Faso, Mali and Niger, especially belonging to the richest quintile makes a difference.

For girls in Niger, the effect of the 5th quintile is even the only statistically significant one. For

Benin and Burkina Faso, the effect of the wealth quintile on enrollment is bigger for boys than

for girls.

In addition to separate regressions for the five countries, an estimation with the combined

dataset but country dummies interacted with all explanatory variables has been conducted. It

allows testing the difference of the coefficients and marginal effects for the countries. Testing

equality of the marginal effects of being female shows that the effect for Senegal is statistically

different than for the other countries. While girls are disadvantaged in Benin, Burkina Faso,

Mali and Niger, the marginal effect of being a girl in Senegal is statistically different from the

effect in the other countries.

Table 9. Average marginal effects of being female on the completion of four years of

schooling.

Delta-methodCount

rydy/

dxp-

value

Benin-

0.189 0.000Burki

na-

0.125 0.000

Mali-

0.130 0.000

Niger-

0.120 0.003Seneg

al0.14

0 0.000Note: dy/dx for factor level is the discrete change from the base level.

21

Table 8 shows the results for the estimation of the completion of four years of schooling.

Again, education of a male and a female household member are related to a higher probability

of having completed four years of schooling. The average marginal effect of being female as

shown by Table 9 now is negative and statistically significant for all five countries:

Teenage girls are twelve (Niger and Mali) to 19 percent (Benin) less likely to have

completed four or more years of school. Testing differences of the marginal effects of the

countries shows that the effect of being female in Benin on completion of four years of

schooling is significantly different from the effect of being female in Burkina Faso, Mali and

Senegal. In contrast to the estimation of enrollment, the marginal effects of being female in an

urban or rural area on four years of schooling do not statistically differ from each other for any

country.8

4 Conclusion

The inclusion of universal primary education into the Millennium Development Goals

has also raised attention for schooling in sub-Saharan Africa. The purpose of this paper was to

shed some light on the schooling patterns of population subgroups and to explore if girls are

discriminated against systematically. The analysis based on DHS data has shown that net

primary school enrollment is still very low in Benin, Burkina Faso, Mali, Niger and Senegal. In

all of these countries, at least a third (Benin) and up to more than two thirds (Burkina Faso) of

children of official primary school age were not enrolled at the time of the surveys. Similarly,

less than half of all young people aged ten to twenty have completed four or more years of

schooling, i.e. are literate in a sustained way. The only exception is boys in Benin, where two

thirds of this age group has attended school for at least four years. Still, the share of young

people that have been to school is much higher than for older cohorts.

The analysis of current school enrollment has shown that boys belonging to a household

of the richest wealth quintile in Benin are the most advantaged group and that boys are

generally more likely to be enrolled except for Senegal.

In addition to a separate regression analysis of the five countries, the estimation has been

conducted jointly to test if the coefficients differ between the countries. Testing equality of the

marginal effects of being female shows that the effect for Senegal is statistically different than

for the other countries. While girls are disadvantaged in Benin, Burkina Faso, Mali and Niger,

the marginal effect of being a girl in Senegal on enrollment is not statistically significant and of

negligible magnitude. The marginal effect of being a girl on the completion of four or more

years of schooling is statistically significant and clearly negative for all five countries.

8 The table is not included in this paper, but available upon request.22

Its magnitude is much bigger than for the estimation of enrollment. This shows that,

although girls still have lower enrollment prospects today, their disadvantage is decreasing.

Even if the goal of universal primary school enrollment will most likely not be achieved by

2015, its aspect of gender equality might well be accomplished in some francophone West

African countries.

References

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Bennell, P. (2002). Hitting the target: Doubling primary school enrollments in sub-

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