Developing Lower Secondary Education: a Rural Issue and Challenge for Sub-Saharan Africa by Agence Française de Développement

Agence Française de Développement

Working Paper

October 2010

Developing Lower Secondary Education: a Rural Issue and Challenge for Sub-Saharan Africa

Alain Mingat and Francis Ndem, IREDU, CNRS and University of Burgundy

Contact: Jean-Claude Balmès, Education and Vocational Training Department, AFD (balmesjc@afd.fr)

Research Department

Agence Française de Développement 5 rue Roland Barthes 75012 Paris - France Direction of Strategy www.afd.fr Research Department

Disclaimer The analysis and conclusions presented in this Working Paper are those of the authors. They do not necessarily reflect the position of the AFD or its partner institutions.

Publications Director: Dov ZERAH Editorial Director: Robert PECCOUD ISSN: 1958 - 539X Copyright: 4th quarter, 2010

Layout: Anne-Elizabeth COLOMBIER

Contents

Introduction

The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

1.1

The Structure of Primary and Secondary Education Cycles

1.3

Different Education Profiles Depending on Countries and Groups of Countries

1.2

Household Surveys Provide Information on Rural Student School Attendance

1.3.1

Marked Differences between French- and English-Speaking Countries

1.4

Possible Differentiation Criteria: Gender, Level of Wealth, Geographic Environment

1.3.2

Illustration of the Great Diversity of Situations: Burkina Faso, Congo, Ghana and Malawi

1.4.1

An Aggregate Perspective for the 29 Countries in our Sample Different Situations According to the Country

Putting the Rural Schooling Lag into Perspective

2.1.1

Often Long Distances to Secondary School for Rural Youth

1.4.2

2.1

2.1.2

The impact of the Distance from the Family Home to the Nearest Secondary School Distances with Consequences on Enrollments

29 29

2.2

The Weight of the Rural Environment in Schooling Dynamics in Lower Secondary Education

Lower Secondary Educational Services in Rural Areas: Variability across Countries

3.1

Specificity of the Organization of Lower Secondary Educational Services in Rural Areas

An Illustrative Simulation

and Leeway for Development

3.2

A Significant Structure of Economies of Scale

The Social and Economic Relevance of Education in Rural Areas

4.1.1

Impacts on the Risk of Poverty

3.3

4.1

4.1.2

4.1.3

4.1.4

The Social Impacts of Education in Rural Areas Impact in Terms of Literacy

Access to School: Impacts and Intergenerational Effects Impacts in Urban and Rural Areas

4.1.5

Impacts on Maternal Health Factors

4.1.7

Towards a Synthesis

4.2.1

A Macro and Temporal Perspective of the Working Population

4.1.6

4.2

4.2.2

4.2.3

56 56 57 58

Impact in terms of Child Health

Rural Areas and the Economic Dimension: the Economic Impacts of Education

Measure and Evolution of Apparent Labor Productivity and its Consequences

61 65

Economic Returns on Educational Investments in Rural Areas

Conclusion

Bibliography

Appendix 1. List of Household Surveys Used in the Study

Appendix 2. Related Tables

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 3

Introduction

The current development of African education systems

(4.2), Chad and Central African Republic (4.4), Burundi (4.6),

years of schooling. This perspective, targeted by the

all countries, and especially in those with a high multiplier

focuses on the perspective of universal completion of six

Mali (4.9), Angola (5.0), Burkina Faso (5.6) and Niger (7.9). In

international community for the year 2015 (Dakar

coefficient, progress towards universal primary completion will

declaration, Millennium Development Goals), will no doubt

generate particularly high pressure, firstly on lower secondary

be reached by a number of countries by that date, while for

education and then on upper secondary. The systems are

others this will probably be somewhat later. However,

potentially subject to massification.

is appropriate to highlight the fact that the growing number

Qualitatively, it is important to firstly identify the nature of

necessarily create strong pressure for the expansion of

potential options for achieving a given quantitative

beyond the very positive aspect of this anticipated result, it of young people completing primary education will

the services to be delivered, since there are in fact many

enrollments in secondary education.

development. This concerns curriculum content, total

teaching hours and schooling conditions, including staff-

The different countries (and the main donors) should

student ratios and the characteristics of the personnel

therefore prepare themselves for this pressure in order to

(particularly teaching staff) mobilized to deliver these

be in a position to propose a relevant response when the

services. Secondly, it is important to take into consideration

time comes, i.e. very soon, since there are already clear

the fact that the population knocking on the door of

signs of such pressure. This response does of course have

secondary education, once the goal of primary completion

both a quantitative (enrollments) and a financial (resources

has been achieved, will differ significantly from the

to be mobilized) aspect; but it also has a qualitative aspect

population completing primary education today. Indeed, in

that is important to take into account.

the present situation, with an estimated primary completion

Quantitatively, it is estimated that the average completion

Sub-Saharan African countries, it is in a way an advantaged

rate of between 50 and 55% on average for low-income

rate was only around 53% in low-income countries in 2003

population (the vast majority are urban dwellers) that has

lead to a multiplication in the number of students then

primary education tomorrow will be very different; there will

observed in 2003 (respectively 7.8 million in 2003 and 20.7

poor and rural children. The latter aspect is often

and that attaining universal primary completion by 2015 would

access to secondary education. Populations completing

completing this cycle by 2.7 compared to the situation

more often be girls, but above all a higher percentage of

million in 2015). This average progression is however of

underestimated and this document intends to explore it in

variable magnitude according to the country, with the

greater detail.

primary education varying between 2003 and 2015 from

The rural dimension of enrollments, particularly at post-

and Togo (1.8), Kenya and Malawi (1.9), to values of over 4.0

education, will be explored from several complementary

multiplier coefficient for the number of students completing

values of under 2.0 in countries such as Uganda (1.7), Ghana

primary level and more especially in lower secondary

in countries such as Ethiopia (4.1), Sudan and Guinea Bissau

angles:

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 5

Introduction

* Firstly, we shall draw up the major factual data on rural

* Finally, we shall tackle the issue of the economic

weight of the rural dimension in the challenges facing the

this concerns both the dimension of demand for

school coverage for the recent period and assess the

impacts of education in rural areas, bearing in mind that

development of education system coverage;

education from the population and that of the

organization of educational services. In this respect, it

* Secondly, we shall explore the issue of the educational

could well be of interest to examine rural schooling

services provided in rural and urban contexts from the

from the angle of the tension between i) education that

point of view of how they are organized and their unit

aims at maintaining populations in their environment

current situation and possible options for the future;

urban environments.

costs by examining in a complementary manner both the

and ii) education that facilitates their mobility towards

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 6

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

First of all, data on the structure of the education systems

systems in the different Sub-Saharan African countries as

Table 1 provides a picture of the structure of the education

concerned.

is not homogeneous throughout the countries in the region.

1.1

far as primary and general secondary education are

The Structure of Primary and Secondary Education Cycles

The first cycle of education (generically called “primary

This variety in the structure and duration of education

education” although the term used may of course vary from

cycles in the different Sub-Saharan African countries is

country to country) is characterized by a variability in the

one of the main points to come out when an

number of years of study it includes, even though this is

international comparison is made of schooling coverage

not exclusively so, since the same duration is observed in

analyzing enrollments and interpreting them in the

usually six years (typical of French-speaking countries but

indicators; it also acts as a reference backdrop for

Gambia, Ghana, Nigeria and Sierra Leone). Some

different countries studied. It is helpful to characterize

countries have a shorter cycle (4 years in Angola and in

the initial overall conditions prevailing in the different

Mozambique) while others, primarily English-speaking

dimension of current enrollments and the challenges

Guinea Bissau, 5 years in Eritrea, Madagascar and

countries in the region before tackling the rural

countries, have a 7-year (Botswana, Lesotho, Namibia,

presented by this dimension for the development

South Africa, Tanzania, Uganda, Zambia, Zimbabwe) or

perspectives of secondary education in Sub-Saharan

even an 8-year cycle (Kenya, Malawi, Sudan).

African countries.

On the basis of the variable duration of primary education,

Table 2 provides information on schooling coverage in

secondary education, lasting between 3 and 7 years of

primary and secondary education for the most recent

exception of a few countries like Kenya, Namibia and Sudan

Saharan Africa. According to these figures, the average

studies, is generally organized in two cycles (with the

available year for all 33 low-income countries in Sub-

which have an undifferentiated single cycle); but, once again,

value of the primary completion rate would be 52.7%. On

there is a wide variety of configurations for the way in which

this basis, and considering an average rate of transition

these two cycles are organized from the point of view of their

between primary and secondary education estimated at

respective number of years of study. In addition, it can be

62.8%, there would be 33.2% of a given generation

automatically incorporates some study years that are part of

(according to each country’s specific definition, given the

observed that when the primary cycle is long (7 or 8 years), it

accessing the first grade of secondary education

secondary education in other countries. As for the duration of

variety in the structure of the educational cycles). The

lower secondary education, this is between two and four

average Gross Enrollment Rate (GER) for the sample of

years, the latter being the more frequent, particularly when the

countries stands at 31.5% for lower secondary and 13.2%

duration of primary education is six years.

for upper secondary education.

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

Table 1.

Structure of the different cycles of general studies in African education systems

Year of study

Angola

Madagascar

P1 P

Guinea Bissau Eritrea

P2 P

Congo

Gambia

Mauritania Nigeria

Rwanda

Sierra Leone Benin

Burkina Faso Burundi

Cameroon

Central African Republic P Chad

Côte d’Ivoire

Republic of the Congo Gabon

Guinea Ghana Mali

Mauritius Niger

Senegal Togo

South Africa Lesotho

Botswana

Zambia

Uganda

Tanzania

P = primary; S1 = lower secondary; S2 = upper secondary

P P

S1 S1

S P P P

S S S

Source: authors, based on data from the UNESCO Institute for Statistics (UIS).

S2 S2

P P

Sudan

Malawi

Kenya

P P

Namibia

Zimbabwe

Ethiopia

Mozambique

Democratic Republic of

S S S

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

Aside from these regional averages, there are significant

it, it ensues that access to secondary education tends to be

Rate (PCR) ranging from 26% (Niger) to 80% (Zimbabwe),

(in Table 2, we can see that the average access rate to

variations from country to country, the Primary Completion

even greater when there is a high primary completion rate

and the primary-secondary transition rate from 28%

secondary education is 24% in the group of countries with

(Tanzania) to 98% (Ghana). One of the consequences of

a low PCR, 36% in the group of countries with an average

this double dispersion is a high variability between

PCR and 46% in the group of countries with the highest

countries in the secondary education access rate, which

PCR).

or more in Congo, Ghana, Togo and Zimbabwe. This

However, this is only a tendency since some countries such

Gross Enrollment Rate in lower secondary education and

group accessing secondary education (16%) in spite of a

ranges from under 15% in Niger and Mozambique to 60%

variability between countries is of course reflected in the

as Tanzania may only have a small proportion of the age

later in upper secondary education. Figures for this

relatively high proportion of the age group completing

indicator range from around 12% (Niger, Mozambique,

primary education (60%), whereas other countries (such as

Tanzania) to over 60% (Congo and Ghana) for lower

Benin, Eritrea and Mali) have a significantly higher

secondary education; as for upper secondary, the variation

proportion of their population accessing secondary

ranges from figures of under 5% (Burkina Faso, Burundi,

education (36.38 and 33% respectively) while a significantly

Mozambique and Niger) to over 20% (Eritrea, Lesotho and

lower proportion of their population benefits from a full

Nigeria). These different orders of variability suggest that it

course of primary education.

worse, a normative) statement on secondary education in

Relatively similar conclusions are obtained when the

would be excessive to try and produce a generic (or even Sub-Saharan Africa; a degree of contextualization is clearly

analysis is extended to the connection between enrollments

necessary.

in lower and upper secondary education.

It is to be noted that there is no correlation between the

One of the characteristics of secondary education is

primary completion rate and the rate of transition between

worthy of mention here. This is the fact that around 20%

the last grade of primary and the first grade of secondary

of all students in each of the two cycles are enrolled in the

education. There is indeed a high variability in the transition

private sector. It is useful to add that, over and above

rate, as noted above, but the level of transition between the

regional averages, highly contrasted situations are

each country) does not seem to depend upon the

countries over 90% of enrollments are covered by the

two cycles (which results from a decision, albeit implicit, by

observed from one country to another. Thus, in some

proportion of the age group completing primary education

public sector at secondary level (Central African

and who are potential candidates for access to secondary

Republic, Eritrea, Kenya, Lesotho, Zambia), while in

education.

others, around half or over half of total students are

enrolled in the private sector (Madagascar, Rwanda,

As the primary completion rate varies greatly from one

Uganda), with every possible situation existing between

country to another and as the transition rate is not linked to

these two extremes.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 9

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

Table 2.

Enrollment indicators in low-income Sub-Saharan African countries (grouped according to PCR); most recent available year Primary Completion Rate (%)

Primary-Secondary Transition Rate (%)

Access rate to lower secondary education (%)

39.1

63.0

23.8

Central African Republic

Burundi

Group 1: low PCR Niger

Burkina Sudan

Madagascar Chad

Guinea Bissau Mozambique Mali

Rwanda Benin

Senegal

Côte d’Ivoire

Group 2: average PCR

Democratic Republic of Congo Eritrea

Mauritania Uganda

Ethiopia Guinea

Sierra Leone Cameroon

26 31 37

37 38

42 46 49

Gross Enrollment Rate Secondary (%) Lower

Upper

22.9

25 33

34 16

21 9 8

56.1

64.2

36.3

34.3

15.9

51 51 53 54

27 21 35

Lesotho

20 20

Group 3: high PCR

Ghana

Tanzania Gambia

9.6

12 2

73.9

60.0

46.7

44.0

16.7

Republic of the Congo

Malawi

Kenya

Zambia Togo

Nigeria

Zimbabwe

Overall

73 73 76 80

52.7

62.8

33.2

45 59 38 53

31.5

17 15 29 13.2

Source: database compiled using Country Status Reports on education systems, previous education sector simulation models (including the model built for the financial analysis of post-primary development options), and the EdStats and UIS database. Demographic data used to calculate enrollment rates comes from the United Nations.

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

1.2

Household Surveys Provide Information on Rural Student School Attendance

School statistics make it possible to describe overall

In order to tackle the analysis of school attendance by

enrollments, bearing in mind that coverage indicators are

students living in rural areas in the education system as

the educational structures to a national reference

a necessity. It should however be noted that the

that the two terms of the ratio may be incorrectly measured.

matter of convention (what are the relevant population

often imprecise in that they relate a population registered in

a whole, recourse to household survey analysis becomes

population. Indeed, there is often good reason to believe

distinction between urban and rural is to some extent a

Concerning school statistics, it is not unusual for them to be

characteristics for classifying a locality as urban or rural?)

the same time). As for demographic reference data,

countries; the general statistics departments in the

either overestimated or underestimated (and even both at 1

and that these conventions are not the same for all

imprecision and errors are also relatively commonplace.3

different countries are in fact likely to adopt somewhat

different criteria for the dividing line. That said, even if the

In view of these possible problems, although measuring

classification criteria are not absolutely alike from one

schooling coverage on the basis of administrative data may

country to another, it is unlikely that this would lead to

often give reasonable orders of magnitude, it is however

invalid comparisons.

liable to be somewhat imprecise. In order to avoid such

possible problems, it is particularly helpful to have recourse

A great deal of household survey data can be mobilized to

on the number of children and the situation of each child in

all the countries), bearing in mind that there are often

to household survey data. Indeed, data is directly available

conduct the analyses (this type of survey exists in virtually

reference to school for each household surveyed; both the

several surveys available in the same country. There are

numerator and the denominator of schooling coverage

basically three types of survey: i) Demographic and Health

indicators are thus available in the same exhaustive source.

Surveys (DHSs), ii) Multiple Indicator Cluster Surveys

Sampling errors do of course exist, but are generally limited

(MICSs) and iii) Surveys based on Core Welfare Indicator

in view of the size of the available household surveys.

Questionnaires (CWIQs); there are also other more specific

1 They may be overestimated because it is in the interest of school heads to declare more students than they actually have in their school. This may be because textbook allocations depend upon the number of students registered in a context where the ministry only provides one textbook for three students; but it may also be because the head’s bonus depends on enrollment statistics. There are also situations of overestimation when the computer file has some schools entered twice.

There is one additional reason why recourse to household

surveys is of particular interest, and that is the availability of

social variables. In school data, the gender dimension is well documented; the social dimension (parents’ profession

2 There may again be underestimations due to behavioral or practical reasons. For example, when tuition fees are received directly by the school and are to be transferred to the inspectorate or the Treasury Department, it may understandably be tempting to underdeclare the number of students. However, there are also practical reasons to do with the fact, for example, that some schools have not sent in the administrative questionnaire (this can concern public institutions, but more often private schools); the school data base may possibly not be adjusted to take these gaps into account.

or level of education), just as the ethnic group or the family’s level of income, is absent. The geographic location is generally indicated (region or province, but not always

the distinction between urban and rural areas), given that it

3 Thus

the most recent population census may be relatively old, meaning that projections may incorporate a significant degree of imprecision (especially when using data per year of age) given the uncertainty as to modifications in fertility and mortality parameters and in fine in the actual growth rates of the different age groups. But census data itself may, before being projected, incorporate some uncertainty (displaced populations, behavior on the part of elected representatives to reduce or increase the municipality’s population, government interest for a higher population than in reality to arrive at a level of GDP per capita that will make the country eligible for specific aid or aid at reduced interest rates, etc.).

concerns the location of the school, not the family. This is not really a problem for primary education as school recruitment areas are generally relatively limited,5 and the

location of the school and the family can be considered as one and the same thing; however, when it comes to

Is the child attending school at present? If so, in which grade? If not, has the child already been enrolled? And if so, what was the highest grade reached? …

secondary education, this is no longer statistically

However, this is not always the case since some urban schools recruit urban students living geographically nearby and also rural students who have to travel a long distance to school due to the absence of a school nearer to their home.

appropriate since some of the students attending lower

secondary schools in town actually come from the country.

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

surveys generally sharing elements in common with the

Enrollment Rate (GER) is overestimated due to the

amongst those actually available.

relatively high).

surveys just listed. We have opted for using the most recent

inclusion of the frequency of repetition (and this can be

On the basis of the content of these surveys, it is of course

As such, the GER is an indicator of capacity (number of

possible to calculate the usual indicators characterizing

places on offer compared to the number of children of the

also possible to calculate them for specific populations, and

indicator.7

schooling coverage in the different cycles of education; it is

relevant age in the country) rather than a coverage

more particularly according to gender, geographic location

In these conditions, it is preferable to calculate an education

(urban/rural) and household level of income (usually looked

at through the income or wealth quintile).

profile that identifies the schooling situation per year of

However, enrollment rates assigned to different study

transition between cycles). This is the perspective we first

study throughout the entire education system (including

cycles are generally no longer considered as the most

use to describe average schooling coverage per country in

relevant statistic for describing schooling coverage. There

the different segments of the education system, then look at

are at least two reasons to suggest that they are

how this overall profile breaks down into variables such as

inappropriate: the first concerns the fact that a single global

gender, rural/urban location and income quintile. This study

indicator is calculated for a cycle, while experience shows

has been conducted for the following 29 countries: Benin,

that a significant proportion of those accessing a cycle often

Burkina Faso, Burundi, Cameroon, Central African

drop out before reaching the final grade of that cycle. Thus,

Republic, Chad, Côte d’Ivoire, Democratic Republic of

an average measure tends to underestimate access to a

Congo, Ethiopia, Gambia, Ghana, Guinea, Guinea Bissau,

cycle and to overestimate (sometimes in very high

Kenya, Lesotho, Malawi, Mali, Mauritania, Mozambique,

proportions) its completion; the second reason is that the

Niger, Nigeria, Republic of the Congo, Rwanda, Senegal,

numerical value of an indicator such as the Gross

Sierra Leone, Togo, Uganda, Zambia and Zimbabwe.

7 It is often suggested that the Net Enrollment Ratio (NER, ratio of the number of pupils of “normal” age for the cycle and the number of children of the same age group in the country) could reduce the drawback of the GER incorporating repeaters; yet in fact, the cure is in a way worse than the disease due to late/early admissions to primary school as is often the case in African contexts, leading to a sometimes very underestimated vision of actual enrollments. There are even more acute problems with the NER at secondary levels in view of the delays accumulated i) in initial access to school and ii) in the course of primary education (repetition) and in access to secondary education (often more frequent repetition of the last grade of primary school in order to increase the chances of being admitted to secondary education).

Household income and consumption are not directly observed in many of these surveys. On the other hand, a fairly wide range of variables characterizing the working population and the household’s actual living conditions is always available. Applying a factorial method to these variables makes it possible to rank the households from the ones with the most spartan living conditions and the fewest assets (characterizing the poorest households) to the ones in the opposite situation with living conditions and assets that demonstrate the implicit existence of a very high income. This is the basis on which all the households observed in a given survey are assigned to one or another of the five wealth quintiles. 6

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

1.3

Different Education Profiles Depending on Countries and Groups of Countries

Table 3 gives the estimations made for these 29 countries.

countries respectively, and 81% and 60.5% survival in upper

Graph 1 provides a visual illustration.

secondary).

Figures are more or less the same as those provided by the

In tertiary education, the gaps (6.1% of the age group

access into first grade of primary school. This is indeed

speaking countries compared to 7.8% in English-

regional average) than in the traditional statistics based on

between the final secondary grade and the first year of

completion, the regional average is slightly below (48%),

compared to 53.3% in French-speaking countries). The

administrative data, with however a marked difference for

have access to tertiary education on average in French-

significantly lower in the household surveys (78.4% for the administrative data

speaking countries) are smaller due to lower transition

(over 90%). Concerning primary

tertiary education in English-speaking countries (44%,

but close to, that provided by the administrative data.

difference is probably to do with the fact that i) the

baccalauréat is already considered as a higher education

1.3.1 Marked Differences between French- and

certificate in French-speaking countries, while this is not

English-Speaking Countries

the case in the English-speaking context, and ii)

universities enjoy much greater autonomy in English-

At the global aggregate level, French-speaking countries are

speaking countries, enabling them to impose more

again found to be lagging behind their English-speaking

regulated access to tertiary education.

counterparts. This gap is visible starting from access to

1.3.2 Illustration of the Great Diversity of

school (88.8% and 71.8% respectively for English-speaking

and

French-speaking

countries).

even

Situations: Burkina Faso, Congo, Ghana and

accentuated for primary completion insofar as there is lower

Malawi

compared to English-speaking countries9 (57.4 and 68.8%

Beyond the regional averages, and although a distinction

access and lower survival in French-speaking countries

respectively). Thus, the proportion of the age group reaching

can be made between the two major groups of countries,

the last grade of primary education is 61.5% in English-

the situation of individual countries can differ even more

speaking countries and only 42.1% in French-speaking

significantly. Graph 2 sets out the education profile

gaps observed between the two groups of countries at the

countries, Burkina Faso, Republic of the Congo, Ghana and

countries: a considerable difference. In lower secondary, the

established on the basis of household surveys for four

end of primary education are also present in lower

Malawi.10

secondary education insofar as the average transition rate

between primary and secondary education is comparable in

the two groups of countries (77% in English-speaking

countries and 80.8% in French-speaking countries) with a

proportion of 35.2% of the age group accessing secondary education in French-speaking countries compared to 47.6%

in English-speaking countries. These differences continue

through to the end of secondary education insofar as

8 Due to the problems encountered with administrative data, we have more confidence in household survey data, to the extent that we estimate that there would in fact be around 20% of an age group on average not accessing primary school in low-income countries in SubSaharan Africa.

English-speaking countries, in spite of a lower transition rate

between the two secondary cycles, have a significantly

higher survival rate than French-speaking countries in upper

9 English-speaking countries where, moreover, primary education comprises on average one more year of study than in French-speaking countries.

secondary (64.9% and 71.2% transition between the two

These countries were chosen to illustrate the variety of situations existing in the different countries in the region.

secondary levels for English-speaking and French-speaking

19.5

66.3

52.0

53.6

Mali

43.2

78.9

24.7

93.5

67.0

Senegal

97.4

Zimbabwe

62.8

61.5

42.1

71.8

88.8

48.0

87.0

78.4

89.6

58.6

69.6

59.9

32.6

82.8

47.6

35.2

38.5

74.3

40.8

41.3

59.5

51.9

27.5

11.1

74.8

62.9

9.8

14.2

32.7

34.2

32.3

43.7

40.0

17.5

35.5

70.1

48.5

17.4

55.2

41.2

17.0

28.6

61.8

12.9

42.2

18.9

Access

38.9

23.4

28.3

58.9

36.0

22.8

36.4

41.2

16.1

8.8

45.0

56.7

5.9

24.5

23.6

29.3

27.6

37.4

8.4

29.0

61.3

41.6

15.4

46.1

26.0

9.4

15.1

29.3

8.0

23.1

10.2

Achèvement

Secondary 1 (%)

24.2

16.6

18.8

7.2

20.2

12.1

23.1

33.7

11.9

7.4

26.8

51.3

4.0

19.7

11.1

21.8

19.8

32.5

6.0

25.8

23.2

33.5

10.6

38.2

22.9

8.1

1.9

21.6

6.0

7.8

17.5

Access

19.8

10.8

14.2

7.1

17.3

11.7

15.8

11.5

7.9

5.3

18.2

45.3

7.0

3.0

19.5

31.2

16.3

19.4

18.5

16.0

4.9

15.9

11.2

3.6

3.8

11.8

7.8

6.1

6.9

5.1

5.6

6.7

10.5

4.8

7.1

12.3

3.6

1.6

5.1

11.0

6.1

12.6

3.0

13.0

6.5

7.5

2.5

Tertiary

Achèvement Access (%)

Secondary 2 (%)

68.8

57.4

59.8

89.3

70.0

59.7

75.6

72.0

48.6

26.4

85.1

93.3

21.6

21.5

49.9

73.3

45.1

64.6

72.6

39.6

65.9

87.7

70.6

31.7

66.5

62.6

42.5

63.8

77.3

29.4

68.2

61.2

Survival Primary

77.0

80.8

78.9

85.4

65.1

70.5

85.4

86.8

84.6

44.8

90.3

79.7

82.2

77.1

83.3

87.1

74.7

68.5

58.5

70.2

88.3

91.5

89.4

77.2

87.3

94.2

77.1

73.3

85.3

66.3

86.0

77.8

Transition primary secondary 2

81.9

63.8

69.6

79.3

88.1

55.2

61.2

79.3

58.4

79.3

90.1 60.1

41.7

74.9

68.9

90.8

63.0

93.7

48.1

81.8

87.4

85.7

88.5

83.6

63.2

55.0

52.8

47.5

61.9

54.7

54.1

Survival Sec.1

64.9

71.2

68.6

12.2

56.1

53.1

63.4

81.9

73.8

83.9

90.5 59.7

67.0

80.4

47.0

74.3

71.8

86.7

71.3

89.0

37.8

80.5

69.1

82.7

88.0

86.6

12.6

73.6

75.5

75.9

76.1

Transition secondary 1 secondary 2

81.0

60.5

69.6

98.1

85.7

96.8

68.2

34.0

67.0

72.0

88.4 67.8

35.5 74.8

89.5

96.1 82.3

75.1

55.2 83.7

42.0

61.0

69.6

51.8

60.0

67.2

49.1

Survival Sec. 2

44.0

53.5

48.7

72.2

32.6

42.7 89.6

61.0

27.0 39.0

51.1

8.1

35.2 31.2

68.3 31.4

81.4 18.8

57.9

63.8

65.3

Transition secondary tertiary

Source: database compiled using Country Status Reports on education systems, previous simulation models of the education sector (including the model built for the financial analysis of post-primary development options) and the UIS and EdStats databases. Demographic data used to calculate the enrollment rates comes from the United Nations.

* Profiles in some countries do not cover the whole system; this is particularly the case when sample size was too small to produce sufficiently reliable figures in the highest segments of the education systems.

countries

English-speaking

countries

French-speaking

29 countries

Average

Zambia

92.1

98.2

Uganda

Togo

83.1

Sierra Leone

Rwanda

97.3

Republic Congo

12.0

84.6

55.3

Nigeria

Niger

18.3

39.2

39.3

85.4

78.6

95.7

63.8

68.4

25.0

40.1

76.7

54.3

22.5

63.3

43.7

22.1

39.0

72.4

Mozambique

Mauritania

Malawi

94.3

98.6

Lesotho

Kenya

63.1

61.0

87.4

Guinea Bissau

Guinea

Ghana

71.0

77.0

Gambia

Ethiopia

95.1

69.8

Democratic Congo

Côte d’Ivoire

Chad

CAR

61.2

93.6

Burundi

Cameroon

49.0

24.4

71.9

39.8

Completion

Primary (%)

Access

Burkina Faso

Benin

Country

Tableau 3. Education profile for a sample of 29 countries, between 2002 and 2005*

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

Graph 1. Average education profile, 28 countries

Source: compiled by the authors based on household surveys listed in Appendix 1.

Graph 2. Education profile in 4 countries

Source: compiled by the authors based on household surveys listed in Appendix 1.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 15

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

There are clear differences. Congo, Ghana and Malawi

(61.1%). During lower secondary education, survival

show high figures for access to primary school (around 90%

registered in Congo (59.7%) is significantly below that of

(40%). However, low survival in primary education puts

this is compensated for in a way by considerably higher

(45% in Malawi compared to 85.1 and 87.7% respectively

(71%) compared to Ghana (38%); a comparable level of

or over), while in Burkina Faso the level is much lower

Ghana (which performs best out of the four countries), but

Malawi in a much lower position than Congo or Ghana

transition between the two secondary cycles in Congo

for Congo and Ghana). Burkina Faso is again in the lowest

access to upper secondary education is also to be found in

position with under 30% completion; this result is admittedly

Malawi (21.8% compared to 23.2% in Ghana) due to high

explained not only by low access but by low survival

1.4

transition (74.3%) between the two secondary cycles.

Possible Differentiation Criteria: Gender, Level of Wealth, Geographic Environment

These education profiles, which differ from one country to

wealth; but this is a relative (rather than absolute)

another, cover the overall school-age population for each

classification since the individuals of a given quintile have

country considered. It is possible that country profiles may

varying levels of wealth depending upon the country

vary more or less according to different groups of

considered. In addition, within a given country, while the

between possible differentiations according to gender,

just like the poor have boys and girls), it is not distributed

population; this is what we are to look at now, distinguishing

level of wealth is of course orthogonal with gender (the rich

geographic environment (urban/rural)11 and the level of

evenly according to geographic environment.

wealth.

The

geographic (urban/rural) environment poses

Before examining the results, it may be useful to point out

specific problems. In the first place, while the distinction

comparable and, more precisely, to note the distinctive

the distinction between urban and rural is on the other hand

that the three dimensions being looked at are not quite

between boys and girls does not lend itself to interpretation,

characteristics of the urban/rural aspect compared to those

a matter of convention: on what criteria, and starting from

concerning gender and the level of wealth. Concerning

gender,

distribution

what size does a village become a town? The problem is that, although the statistics departments in all countries are

largely

exposed to the same question and attempt to respond in a

homogeneous across countries and groups of population

reasonable manner, they do not necessarily give exactly

(groups defined according to the other distinctive criteria).

the same answer.12 At the end of the day, it has to be said

between 50% girls and 50% boys within the school-age

conventional nature and that the convention selected in one

that the distinction between urban and rural is of a

In all countries, distribution is more or less balanced

country may differ from that selected in another. In this

population. This distribution is the same in urban and rural

context, the international comparison of figures that make

populations, and in wealthy and poor families. The

the distinction between rural and urban in analyzing a given

level of family wealth is generally looked at

phenomenon (here education) is therefore not ideal; this

suggests that care must be taken when conducting such

through a grouping per quintile based on a previous ranking

of the families in a country (or of a sample of families in a

comparisons, but not that it would be pointless to do so.

assets in their possession and standard of living. As the

11 There may also of course be regional or ethnic disparities; however, these are not taken into

household survey) according to the level of consumption, grouping is made within each of the countries studied, this

account here as they cannot be analyzed in terms of international comparison.

For example, in one country a built-up area with a population of 3 000 is urban, while in another, this definition will be used for built-up areas with a population of 5 000, and at the same time the very concept of a built-up area is also a matter of convention.

provides a homogeneous distribution in five groups each

representing 20% of the population, arranged by order of

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 16

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

Caution is all the more important in that, while countries

(mechanically) differ too much from the national average;

identification of urban and rural, they also considerably

of the overall population.

may of course differ on the basis of the statistical

this will naturally be less true if rural areas account for 45%

differ on the actual respective weight of the two

environments, bearing in mind that these aspects are

A third difficulty with the geographic variable is its certainly

data. Thus, on the basis of the conventions selected in the

variable. The wealth variable is in fact distributed very

combined and cannot be separated in existing comparative

imperfect yet substantial relation to the family income

different countries for the urban-rural distinction, it can be

differently in urban and rural areas. Table 4 indicates the

seen (Table A1, in the appendices) that, on average for the

aggregate distribution of the population for the 29 countries

29 countries analyzed, 30.2% of the school-age population

in our study sample.

live in urban areas whereas 69.8% live in rural areas.

Generally speaking, the majority of economically privileged

However, this is an average since in some countries, like

families live in urban areas (52.6% of urban dwellers belong

Congo and Côte d’Ivoire, more than half the school-age

to the richest quintile, and 80% to the two highest quintiles),

population live in urban areas (according to the conventions

while an even larger majority of those living in poverty are

adopted in these two countries), while in countries such as

rural dwellers (96.5% of the poorest families live in rural

Burkina Faso, Burundi, Ethiopia, Kenya, Malawi and

areas). That said, there is indeed also a segment of poor

rural areas. It is of course essential to be aware of this data

bearing in mind that some rural families are not, in relative

Uganda, 85% or more of the school-age population live in

families who live in towns or their immediate vicinity,

and bear these differences in mind when analyzing

terms, in the category of underprivileged families (62% of

enrollments according to the geographic environment in the

families belonging to the second richest quintile and 24%

different countries in the region. For example, in a country

belonging to the highest quintile live in rural areas). The

where rural areas account for 85% of the country’s total

latter remarks however only slightly modify the initial

population, the schooling situation of rural children cannot

observation.

Table 4. Distribution of the population according to geographic environment and level of wealth (Sample of 29 countries) Environment Wealth quintile

Number

20% poorest

105 027 396

Quintile 3

88 194 738

20% richest

25 261 327

Quintile 2 Quintile 4 Total

99 436 506 65 812 328

383 732 295

Rural % vertical 27.4%

% horizontal 96.5%

3 822 827

23.0%

82.8%

18 278 166

6.6%

24.4%

78 308 694

25.9% 17.2% 100.0

92.3% 62.1%

72.0%

Urban % vertical

Number

2.6%

8 254 933

17.2%

106 472 904

20.0%

100.0%

52.6%

75.6%

103 570 021

19.4%

100.0%

100.0

7.7%

37.9% 28.0%

107 691 439 106 029 263 532 613 850

20.4%

% horizontal

12.3%

Source: compiled by the authors based on household surveys listed in Appendix 1.

Total % vertical

108 850 223

27.0%

148 881 555

Number

3.5%

5.5%

40 216 935

% horizontal

20.2% 19.9% 100.0

100.0%

100.0% 100.0% 100.0%

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

Bearing in mind these introductory remarks, we can now

Table 5 sets out the basic estimations of the overall education

attempt to present the estimated education profiles for the

profile for the 29 countries as a whole, distinguishing the

different groups of population considered. We shall

population groups according to gender, geographic

examine the aggregate situation for the 29 countries in our

environment (urban/rural) and level of family wealth. For the

sample first, before examining that of the different countries

latter variable, we have grouped together the two poorest

1.4.1 An Aggregate Perspective for the 29

quintile (Q5) is considered separately. The table also indicates

making up the sample.

quintiles (Q1 and Q2, named Q12); we have done the same

for quintiles 3 and 4 (variable named Q34); the wealthiest

Countries in our Sample

the main combinations of the three segmentation variables.

Graph 3 provides an initial illustration of same.

Graph 3. School profile by gender, geographic environment and level of wealth, 29 countries

Source: compiled by the authors based on Table A1 in Appendix 2 and the household surveys listed in Appendix 1.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 18

59.8

Primary Survival

1.9

34.9

Primary Survival

49.4

53.6

0.5

62.9

61.3

47.9

2.2

6.4

9.1

15.5

24.7

37.1

72.5

Gender

GRQ34

67.9

76.8

58.3

5.7

11.5

16.3

25.6

35.4

45.5

75.5

76.2

71.6

66.9

8.7

17.9

22.9

32.6

42.8

57.4

82.6

GRQ5

70.8

80.6

61.1

8.4

16.9

21.4

30.9

41.5

50.4

81.1

54.1

60.1

40.2

1.3

3.8

5.7

18.5

28.8

69.8

BRQ12

57.5

71.1

48.0

2.8

7.5

10.0

17.3

27.2

36.9

72.9

67.6

67.3

4.2

10.9

14.4

21.3

31.5

44.1

79.5

78.6

76.7

73.5

13.2

26.2

30.9

40.2

51.1

65.6

87.9

49.4

63.9

41.0

0.8

3.3

5.9

11.4

20.2

29.9

68.6

Q12

52.8

59.6

48.9

1.2

3.1

6.3

12.1

37.4

76.4

64.5

74.9

57.3

5.0

11.6

16.5

26.9

37.8

48.1

80.6

Wealth quintiles Q34

67.1

67.6

62.5

10.1

15.7

25.1

37.4

54.1

85.5

76.3

87.7

77.1

17.5

31.0

37.9

50.8

63.4

71.4

91.8

Gender x geographic context x wealth quintiles BRQ34 BRQ5 GUQ12 GUQ34

75.3

85.7

74.1

12.6

25.1

32.8

45.8

58.7

68.2

91.6

Geographic context R U

77.6

77.4

82.3

25.6

34.3

46.2

59.5

76.9

93.1

56.8

65.5

55.4

2.6

6.2

10.2

17.4

30.6

45.9

83.2

BUQ12

61.1

73.7

50.2

3.4

9.2

12.4

20.4

30.5

39.7

76.3

70.9

72.7

69.4

7,7

16.8

22.6

46.5

63.1

90.8

BUQ34

74.5

83.9

72.5

10.4

21.0

29.7

42.9

55.3

65.5

89.8

Source: compiled by the authors based on Table A1 in Appendix 2 and the household surveys listed in Appendix 1.

80.0

81.9

88.1

19.7

36.5

43.8

55.1

68.9

84.6

96.0

BUQ5

76.0

87.0

75.6

14.8

29.0

36.1

48.6

61.9

70.7

93.3

Gender x geographic environment BR GU BU

GUQ5

50.9

67.0

44.5

2.3

5.6

7.3

13.8

23.5

33.5

69.3

* G is for “girls”, B for “boys”, R for “rural”, U for “urban”, Q12 for quintiles Q1 and Q2 combined (the 40% poorest population of each country), Q34 for quintiles Q3 and Q4 combined, and Q5 for the richest quintile.

Secondary 1 Survival

Primary-Sec.1 Transition

Tertiary Access

3.1

Secondary 2 Completion

Secondary 2 Access

6.5

13.2

23.1

62.2

GRQ12

69.6

78.9

6.9

Secondary 1 Completion

Secondary 1 Access

Primary Completion

Primary Access

29 countries overall (%)

Secondary 1 Survival

Primary-Sec.1 Transition

Tertiary Access

18.8

14.2

Secondary 2 Completion

Secondary 2 Access

28.3

38.5

48.0

78.4

Overall

Secondary 1 Completion

Secondary 1 Access

Primary Completion

Primary Access

Groups of population* 29 countries overall (%)

Table 5. Education profile elements by gender, geographic environment and level of wealth, average values for the 29 countries

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

The three segmentation variables are seen each to have a

For gender and geographic context, there are only two

taking of course the expected direction: girls are in the

as this is constructed and part of a continuum. For the latter

significant impact on education profiles, with disparities

“natural” categories; this is not the case for the level of wealth,

background compared to boys, rural dwellers in the

variable, Table 6 sets out three comparisons: the first

background compared to urban dwellers and the poor in the

compares the schooling of individuals belonging to quintiles 1

background compared to the rich. If we focus more

and 2 combined, with that of individuals from the fifth quintile

specifically on the dimension of geographic location, rural

(the poorest 40% and the richest 20% respectively within

right from the start of primary school (average access rate

schooling of individuals belonging to the first and second

widens with the higher levels. Thus, urban children are over

to the third and fourth quintiles; finally, the third comparison

education.

poorest quintiles with that of individuals belonging to the other

children are seen to be behind their urban counterparts

each country’s population); the second contrasts the

of 72.9 and 91.6% respectively) and the gap progressively

quintiles (the poorest 40%) with that of individuals belonging

twice as likely as rural children to enter lower secondary

again contrasts the schooling of individuals from the two

three quintiles (the 60% richest/least poor). Naturally, the

The gaps connected to the three social segmentation

greater the gap between the groups compared (the poorest

variables are not of the same intensity. In order to measure

group compared with the richer groups), the greater the

relative gaps.13 To remain with relatively similar references to

this intensity, Table 6 provides a measure of the disparity

the other two social variables, we are to opt for the third

ratio between privileged and underprivileged groups at

comparison contrasting the poorest 40% with the richest/least

different points of the education profile (aggregate values

poor 60% of the population.

for the 29 countries).

Table 6. Disparity ratio of privileged to underprivileged groups in diverse points of the education profile: gender, geographic environment and level of wealth, 29 countries Social groups

Primary Access

Primary Completion

Secondary 1 Access

Secondary 1 Completion

Secondary 2 Access

Secondary 2 Completion Tertiary Access

Primary Survival

Primary-Secondary 1 Transition Secondary 1 Survival

Gender Boy / Girl 1.07 1.11

1.17

1.20

1.31

1.47

1.48

1.05

1.04

Geographic environment Urban / Rural 1.26 1.85

Q5 / Q12 1.34 2.39

2.16

3.14

2.64

4.45

3.28

6.45

4.44

22.31

1.21

1.37

3.37

9.43

1.54

1.88

1.31

1.54

Wealth quintiles Q34 / Q12 1.17 1.61

1.87

2.36

Q345 / Q12 1.23 1.85

2.23

2.93

2.81

3.74

6.36

9.88

3.52 1.40

1.17

1.30

4.94 1.56

1.24

1.38

Source: compiled by the authors based on household surveys listed in Appendix 1.

13 For example, at the end of upper secondary education, the ratio registers at 3.11 in the second comparison (poorest 40% compared to 40% less poor, without the richest 20%), while it registers at 4.63 in the third comparison (poorest 40 % compared to richest/less poor 60%), and at 7.67 in the first comparison contrasting the situation of the poorest 40% with that of the richest 20% of each country’s population.

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

Generally speaking, and that is true for each of the three

favorable characteristics (boy, urban, rich) and the one

between privileged and underprivileged groups tends to be

The second part of Table 5 gives the estimated figures

profile; thus, social disparities grow as we climb up the

the 12 categories identified by the different combinations of

segmentation variables, the intensity of the disparities

accumulating all the unfavorable factors (girl, rural, poor).

more pronounced the higher the position on the education

needed to build the education profiles specific to each of

rungs of the education system. This tendency is visible in

the three variables.

Graph 4. This graph also shows that the intensity of the disparities between privileged and underprivileged groups

If we examine the education profiles of the two extremes

is slightly more pronounced on the basis of geographic

in the first instance (rich urban boys on the one hand and

environment than on gender. Thus, at the point of access to

poor rural girls on the other), then it is clear that schooling

lower secondary education for example, the disparity ratio

situations are extremely contrasted. Thus, for the 29

of the privileged group to the underprivileged group is 1.17

countries as a whole, while 96% of individuals have

fact the level of family wealth that makes the biggest

62% of individuals in the second group. If we now take

education, the disparity ratio between privileged and

interest in the context of this study), there are already

for gender and 2.16 for geographic environment. But it is in

access to school in the first group, this is the case for only

difference. Thus, again on access to lower secondary

access to lower secondary education (of particular

underprivileged groups reaches 2.23. At this point in the

considerable differences between the two categories with

system, disparities according to urban/rural areas and level

a figure of 68.9% for the first group and only 13.2% for

of wealth are roughly around six times greater in intensity

the second. As for access to upper secondary education,

than for gender. The ranking of the weight of the three

this is the case for 43.8% of rich urban boys compared to

segmentation variables is clearly visible in Graph 4.

only 3.3% of poor rural girls. The gaps are considerable.

Graph 5 illustrates these observations by extending the

Combining the modalities of the three social variables does

comparison to all 12 categories with, at either extremity,

of course result in even more pronounced disparities,

the two extreme cases that have just been briefly

particularly between the group that accumulates all the

described.

Graph 4. Social disparities in diverse entry and exit points of the education profile, 29 countries

Source: compiled by the authors based on household surveys listed in Appendix 1.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 21

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

Graph 5. Education profile by comparing gender-geographic environment and level of wealth (29 countries)

Source: compiled by the authors based on household surveys listed in Appendix 1.

1.4.2 Different Situations According to the Country

the three dimensions in each national context, targeting

The information just presented concerns the average

focuses on the disparities between urban and rural areas

more particularly the urban/rural distinction; iii) the third

situation of the 29 countries in our study sample. It is likely

and examines possible differences between countries in

that the major lines that have been identified are more or

the process of generating these disparities between the

less valid for all of these countries; but it is also probable

different segments of the education system.

that some specific characteristics apply to the situation of

the individual countries making up the sample and these

* Variable intensity in the overall intensity of social

involved, we are to limit this presentation to the education

being considered

are worth exploring here. In view of the extent of data

inequalities and in the weight of the three social dimensions

profile for primary and lower secondary education. These education profiles (primary and lower secondary access

For practical reasons, it is not possible to take stock of the

social segmentation variables, are set out in Table A1 in

the education profile (access to primary, primary

along three complementary lines: i) the first consists in

conduct the analysis at the end of lower secondary

and completion) per country, and according to the three

social disparities in each country at each distinctive point of

annex to this document. On this basis, we shall proceed

completion, access to secondary…). We have decided to

calculating social disparity ratios for the three dimensions

education (a point where social inequalities have had the

analyzed and in building a global measure of the social

time to “express themselves”, and not take into account the

ii) the second consists in identifying the respective weight of

more intense but also where coverage is generally low).

disparities in the different countries making up the sample;

highest levels of the system, where inequalities are indeed

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

Table 7 offers a synthetic view of the intensity of the social

between the two orders of magnitude formally confirms this.

disparities per sample country for each of the three

dimensions studied, measured here by comparing the

On average, countries where the education system has

chances of the privileged population (boys, urban, two

lower coverage therefore have a significant tendency to be

secondary with those of the underprivileged population

does not however mean that the relationship is necessarily

highest wealth quintiles) of accessing the final year of lower

characterized by a higher level of social disparities. This

(girls, rural, three lowest income or wealth quintiles).

rigid and deterministic. Indeed, some countries may be

clearly above or below the average, demonstrating a

Let us focus firstly on the last column in the table, which

specific propensity for producing greater or lesser social

provides the average value of the ratio of the respective

inequalities when the influence of the level of schooling

social dimensions being studied.14 The average value of

like Benin, Burkina Faso, Côte d’Ivoire and Rwanda are

chances of privileged to underprivileged youth in the three

coverage is controlled in the different countries. Countries

this indicator for the sample countries as a whole is 3.91;

thus characterized by a significantly lower level of social

Thus, values of under 2 are observed in Ghana (1.54, the

coverage of their education systems; on the other hand,

but there is also noteworthy variability across countries.

disparities than would have been expected from the level of

minimum out of all the sample countries), Côte d’Ivoire

social disparities are higher than anticipated on the basis of

(1.9), Kenya (1.8) and Nigeria (1.7), while values of around

the level of coverage in Cameroon and Guinea and even

5 or over are registered in Burundi (5.0), Ethiopia (7.3),

more so in Ethiopia and Guinea-Bissau.

Guinea-Bissau (18.2, the country where social disparities

are the most pronounced according to the selected

* The respective weight of the three social dimensions,

indicator), Mozambique (8.6) and Central African Republic

particularly the urban/rural distinction

(6.0).

The overall average level of social disparities differs from

This variability in the intensity of social disparities between

country to country; as the global index incorporates

countries can itself be explained by the distinctive

disparities according to gender, geographic environment

political aspects do of course come to mind. The possibility

different situations for the three components. Data recorded

characteristics of each country; cultural, contextual or

and the level of wealth, the overall score can result from

of these factors actually playing a role cannot be ruled out;

in Table 8 enables an initial approach to this issue. For

but it can also be considered that the rule often validated in

some countries, the level of disparities is fairly coherent for

sociology of education whereby social disparities tend to be

the three dimensions studied, but this is not the case for all

high when systems are not well-developed (the chances of

countries:

schooling are low and the most privileged segments of society tend to monopolize the few places available) and

decrease as schooling coverage increases (and segments of the population previously excluded are admitted to

school). Graph 6 has been built on the basis of this

hypothesis.

The distribution of the dots, representing each country at the intersection between the rate of lower secondary completion and the overall index of social disparities at the

same level of study, shows the empirical relevance of the

14 This statistic does not have specific properties; it does however give a good overall idea of the intensity of overall social disparities (at the end of lower secondary education) in the different countries.

hypothesis; the value (0.60) of the coefficient of determination (R²) characterizing the average relation

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

Table 7. The extent of social disparities in the different countries (end of lower secondary) Country

% Rural

% Access End Secondary 1

Benin

23.1

Burundi

8.8

Burkina Faso Cameroon

Central African Rep Chad

Côte d’Ivoire

Democratic Congo Ethiopia Gambia Ghana

Guinea

Guinea Bissau Kenya

Lesotho Malawi Mali

Mauritania

Mozambique Niger

Nigeria

Republic Congo Rwanda

Senegal

Sierra Leone Togo

Uganda Zambia

Zimbabwe

Average 29 countries

10.2

29.3

12.7

12.1

26.0

32.3

20.8

41.6

61.3

29.0

10.5

37.4

27.6

34.6

23.6

22.0

8.3

Gender (B/G) 1.89

1.38

1.00

0.93

1.71

2.17

1.24

1.28

0.95

1.09

1.01

1.35

Ratio chances of privileged/underprivileged Environment Wealth Average (U/R) (Q12/Q345) 2.69

1.80

2.12

6.75

7.14

4.96

6.88

3.94

9.69

8.31

1.70

2.23

4.56

6.85

6.48

3.78

2.80

1.86

8.92

12.00

1.54

2.06

2.83

3.56

2.75

5.92

4.27

3.91

5.96

4.76

1.91

1.79

7.29

2.22

1.54

3.61

1.65

18.67

34.33

18.22

0.81

2.44

3.90

2.38

1.44

1.28

1.79 1.11

1.83

1.86

2.58

4.10

3.39

2.20 3.11

3.47

4.23

8.42

15.67

1.83

2.32

3.12

2.91

8.64

39.7

1.11

1.57

2.33

1.67

14.0

1.01

4.98

2.56

2.85

68.2 69.7

45.0

17.7

41.2

36.4

22.8

36.0

58.9 28.0

1.10

1.32

0.99

1.40

1.64

1.25

1.08 1.3

Source: compiled by the authors based on household surveys listed in Appendix 1.

2.95

4.90

3.00

2.77

2.90

2.92

1.22 4.6

3.36

5.89

2.27

3.09

5.51

4.22

2.14 5.6

2.47

4.04

2.09

2.42

3.35

2.79

10.48 3.8

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

Graph 6. Overall index of social disparities at the end of lower secondary according to the cycle completion rate

y = 40.383x R = 0.6005

-0.8015

Source: compiled by the authors based on household surveys listed in Appendix 1.

Benin and Mali, disparities according to geographic

Rwanda, Senegal, Sierra Leone, Togo and Zambia,

environment and the level of wealth are moderate or low

disparities observed in the three dimensions are small, or

while gender disparities are particularly pronounced;

Burkina Faso, on the contrary, the urban/rural

Burundi and Ethiopia, the situation is the opposite to

are within the regional average, and do not demonstrate

an unfavorable balance to the detriment of any particular

one.

dimension is problematic;

Focusing specifically on rural and urban disparities, the countries where this dimension plays a particularly

that in Benin with very low or non-existent boy-girl

important role (to the detriment of rural children) are first of

disparities and very substantial disparities according to

all Central African Republic, Ethiopia, Guinea-Bissau and

geographic environment and level of wealth;

Mozambique, followed by Burkina Faso and Burundi; in

contrast, the dimension of disparities between urban and

Cameroon, it is household wealth that makes the

rural environments seems to be better managed in Côte

difference, the urban/rural distinction having a moderate

d’Ivoire, Ghana and Nigeria.

influence and the gender distinction a very limited one;

Finally, it is important to note that the lag in rural

Chad, there are considerable disparities according to

enrollments is very closely connected to the level of the

gender and geographic context, and less significant

system’s quantitative development. Graph 6 showed that

disparities according to the level of household wealth; In

overall social disparities are all the more pronounced

when schooling coverage is weaker; analyzing the

Guinea-Bissau, Mozambique and the Central African

connection between each of the three social dimensions

Republic, there are very high social disparities in all three

and coverage shows that it is by far the urban/rural

Gambia, Guinea, Kenya, Malawi, Mauritania, Nigeria,

observation. Indeed, the coefficient of determination is

dimensions, while in the Republic of the Congo, Ghana,

dimension which is at the origin of this overall

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

only 0.17 with gender and 0.40 with the level of wealth,

Referring to the data in Table 5, we know that 17.3% of rural

This brings us back to the argument put forward earlier,

education. It is consequently of interest to describe the

while it registers at 0.68 with the urban-rural distinction.

and 45.8% of urban dwellers complete lower secondary

and which will be handled again in point 2.2 below,

selective process leading up to this situation and, in

according to which education systems develop from the

particular, to identify the respective weight of the different

easier to more difficult contexts, starting with densely-

segments earlier in the education system where the

education makes it easy to set up schools, continuing

a breakdown of the geographic disparities observed

populated urban areas where the high demand for

disparities were progressively generated. Table 8 provides

with areas where the density of population remains

between urban and rural areas at the end of lower

acceptable and ending with sparsely populated rural

secondary education.

areas where the number of school-age children is limited and where demand for schooling is uncertain.

This descriptive table provides us with some very useful

* Process of generating urban/rural disparities in the

production of schooling inequalities between urban and

information that gives an understanding of both the rural environments and on schooling dynamics in Sub-

different sample countries

Saharan African countries. Upon examining the situation at

In this study, we focus specifically on the differentiations

the end of lower secondary education, we indeed expected

between rural and urban areas and on lower secondary

that the geographic differentiations at this level had already

education. It is however important to situate the study in the

been initiated to some extent in primary education, but the

framework of the sector as a whole, especially for the

common opinion was also that the transition between

overall social differentiations existing in the education

primary and secondary education would be particularly

systems of the different countries studied.

unfavorable to rural populations.

Table 8. Breakdown of the urban-rural differentiations observed at the end of lower secondary education between the different segments of the education profile 29 countries

Primary Access

% of the age group enrolled Rural (%) Urban (%) 72.9

91.6

Primary Completion

36.9

68.2

Secondary 1 Completion

17.3

45.8

Secondary 1 Access

27.2

Multiplicative ratio urban/rural

Specific contribution

Relative contribution %

1.85

1.47

39.7

2.64

1.23

21.0

1.26

58.7

2.16

Source: compiled by the authors based on household surveys listed in Appendix 1.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 26

1.26

1.17

23.4

15.9 100.0

1. The Overall Situation of Lower Secondary Education in Sub-Saharan African Countries

Factual information on the 29 countries as a whole is at

secondary survival. It is therefore due to the fact that rural

variance with this idea. In fact, almost two-thirds (63.1%)

children have less access to primary education and

secondary education between urban and rural dwellers

found less often than their urban counterparts at the

of the differentiations observed at the end of lower

above all drop out early more often that they are to be

are generated by unequal access to primary education

outcome of lower secondary education; transition to

(23.4%) and above all by different survival rates

secondary education is indeed a little more difficult for

throughout the primary cycle (39.7%); on this basis,

them, but represents only a small share of the sum total

transition to secondary education adds only 15.9%, with

(in all it accounts for only 15% of rural/urban disparities

a slightly higher figure (21%) estimated for lower

observed at the end of lower secondary education).15

Diagnostic errors were registered in the 1980s, especially in the Philippines and Morocco, giving more weight to primary-secondary transition than in reality; this led to inappropriate educational policies and unconvincing results for the systems.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 27

2.1

Putting the Rural Schooling Lag into Perspective

The impact of the Distance from the Family Home to the Nearest Secondary School 2.1.1 Often

As we have seen, rural enrollments in lower secondary

Long

Distances

School for Rural Youth

education are behind those of urban youth. This is true in

all the countries studied; but the intensity of same varies

Secondary

In town, distance to school must be taken into

from one country to another; we have already identified

consideration, but on the one hand is generally shorter than

more intense the lower the schooling coverage,

public transport makes somewhat long distances less of a

the fact that urban-rural disparities tend to be significantly

in the country and, on the other hand, the existence of

highlighting a process of “sequential” development of the

disadvantage. Table 9 indicates the distribution of the

systems, targeting initially those populations easiest to

distance to the nearest secondary school, for rural and

enroll and progressively those that are less so. We are

urban children respectively in a sample of 12 countries.

to the analysis of the lag in rural schooling: the fact that

Let us examine the averages regarding the time separating

distance from home, which no doubt constitutes an

significant differences between urban and rural contexts in

now to provide a new, yet related, explanatory dimension,

for those children, secondary school is often some

home from the nearest secondary school. There are very

additional impediment to their enrollment. In order to

the 12 countries in this sample, the former benefiting from

distribution of the distance to the nearest lower

minutes) from home on average, while for the latter, the

find out to what extent the fact that the (nearest)

average (69 minutes). While 36% of urban youth have a

document this issue, we shall describe firstly the

a lower secondary school less than half an hour (27

secondary school for rural youth; next, we shall strive to

nearest school is almost one hour and ten minutes away on

secondary school is far from the family home diminishes

lower secondary school less than 15 minutes from home,

especially amongst the population that has completed a

countries analyzed. Graph 7 adopts a cumulative

the chances of having access to secondary education,

this is the case for fewer than 8% of rural youth in the twelve

full course of primary education.

perspective.

2. Putting the Lag in Rural Schooling into Perspective

Table 9. Distribution of the distance to the nearest secondary school in 12 countries Time to school Environment Benin

Burkina Faso

0-15 min. Rural Urban 8.4

13.6

15-30 min. Rural Urban

30-45 min. Rural Urban

45-60 min. Rural Urban

11.4

18.2

10.1

15.2

15.0

2.0

14.9

26.3

3.2

34.2

10.0

40.0

Congo

19.7

49.8

17.1

26.1

Guinea-Bissau

14.7

Burundi Guinea

Lesotho

7.1 6.0 9.4

59.1 21.5

Malawi

4.8

31.3

Mauritania

3.1

27.2

Mali

9.9

58.7

14.2 5.0

9.5

13.9

29.2 36.6

11.6

15.1

4.7

3.9

16.6

28.8

9.4

21.8

8.9

8.1 4.3

1.9

12.3

4.0

46.0

1.8 8.8

21.0

55.6 80.0

69.9

47.9

5.1

100.0

53.4

21.4

0.4

11.2 17.6

4.0

25.1

3.7

22.5

4.3

16.7

84.8

4.4

6.6

83.6

4.2

6.4

28.9

13.8

18.8

5.8

Average

7.6

35.7

9.9

28.8

10.2

19.4

6.4

3.7

34.6

5.3

22.3

3.9 7.4 7.1

71.2

100.0 100.0

100.0

100.0 100.0

100.0

8.5

100.0

6.4

100.0

1.3

71.6

13.5

65.9

8.9

Source: authors, based on household surveys, QUIBB in particular, which adequately document spatial aspects.

39.9

23.3

58.9

17.9

63.6

70.9

7.8

6.8

100.0

10.4

18.1

100.0

19.7

7.9

Overall Average time (min.) Rural Urban Rural Urban

3.5

14.2

31.4

30.1

65.4

23.6

2.4

2.8

56.2

4.0

11.6

Rwanda

Senegal

10.3

60 min. or more Rural Urban

100.0 100.0

100.0

63.6 77.6

68.6

59.6

15.9 31.6

66.8

33.3

100.0

81.1

31.7

100.0

80.7

27.0

100.0 100.0

100.0

71.3 74.2

69.3

18.3 30.6

27.3

Graph 7. Cumulative % of time for the nearest secondary school by environment

Source: compiled by the authors based on household surveys listed in Appendix 1.

Data registered in the graph show that only 18% of rural

This overall average situation prevails in all the sample

youth have a lower secondary school less than 30 minutes

countries; significant differences are seen however from

two out of every three urban children. The nearest

geographic configuration of the different countries and ii)

only a third of rural youth in the sample countries; this is the

areas, and also iii) the schooling coverage situation in

from their parents’ home, while this is the case for around

one country to another; they are partly due to i) the

secondary school is under one hour from home for around

the distribution of population, particularly within rural

case for over 90% of urban youth.

secondary education in rural areas and iv) policies

2. Putting the Lag in Rural Schooling into Perspective

selected in terms of school mapping (either small local

to lower secondary school. The figures are low since some

further from potential students). Thus, the average

attended school but have not completed primary education.

schools or bigger schools that are, as such, generally

children have never attended school and others have

distance to secondary school in rural areas is under one

Finally, some children were able to complete primary

hour in Congo and Lesotho, while it exceeds one hour

education but had to put an end to their studies at this point.

of the Congo, almost 38% of rural youth have a lower

latter population.

while this is only the case for under 10% of rural youth in

In this sample of countries (but this is probably generally

twenty minutes in Mauritania or Senegal. In the Republic

The second part of the table more specifically targets the

secondary school less than thirty minutes from home,

Mauritania, Rwanda and Senegal. 2.1.2 Distances Enrollments

with

Consequences

the case), the distance to lower secondary school clearly

has a negative impact on the individual chances of access.

First of all, let us take a look at the issue of transition

between primary and lower secondary education. While the

chances of transition register at 69% on average for

We shall now strive to determine to what extent the fact that

children who have a school in the geographic neighborhood

the lower secondary school is farther from or nearer to

(less than 15 minutes away), this figure drops by 10 points

home modifies individual behavior in terms of access to

(59.1%) when the school is 15 to 30 minutes away, to

lower secondary education. It may be assumed that the

51.2% for 30 to 45 minutes away and is only 45.4% when

costs money-wise (transport, food…) for families, timewise

nearest school consequently clearly demonstrates a

before/after school) and psychologically (control over

“on the spot”, a low transition rate demonstrates that some

existence of a lower secondary school locally implies lower

the distance exceeds 45 minutes. A long distance to the

(possibility of using children for domestic chores

difficulty on the school supply side. But when the school is

children, especially girls) than those entailed by enrollment

individuals decide not to continue their studies in spite of

in a school far from the family home. It is necessary to

the logistic ease with which they could do so. The difficulty

enrollment and what the relation is between enrollment and

level of study.

determine at what distance difficulties result in lower

in this case consequently lies in education demand for this

the distance from home to the nearest lower secondary school.

This structure is valid on average for the sample of ten

countries, although there are noteworthy differences

To tackle these questions empirically, we have recourse to

between countries. These differences concern both the

econometric estimations on the basis of ten countries

level of the transition rate and the intensity of the relation

where household surveys include information on the

between distance and probability of transition. As we wish

distance from home to primary and lower secondary

to target the latter component, we have calibrated at 100

countries in our sample. It gives the result of the

individuals with a school less than 15 minutes away.

secondary education depending on the distance between

procedure. The differences between countries are clearly

schools respectively. Table 10 targets rural youth in the ten

the transition rate observed in each country for

estimations analyzing the probabilities of access to lower

Graph 8 illustrates the results obtained from this

the family home and the nearest secondary school, on the

apparent, with on the one hand countries where distance

overall school-age population on the one hand and on the

has no proven influence on transition, such as Congo, or

other.

countries like Mauritania and Rwanda where the fact that

population having completed primary education on the

little influence like Benin or Burundi, and on the other, secondary school is far from the family home proves to

The first portion of the table covers the total school-age

greatly penalize the chances of transition from primary to

population and examines the individual chances of access

secondary education.

2. Putting the Lag in Rural Schooling into Perspective

Table 10. Probability of access to lower secondary education according to the distance to school (rural environment only) Access (%) (of total population) Benin

Burkina Faso

Burundi

0-15 min. 23.5 19.0

5.6

Distance: home - lower secondary school 15-30 min. 30-45 min. 23.5

20.2

9.6

7.3

5.6

4.0

45 min. or more 8.9 2.3

1.9

Congo

34.7

28.8

19.1

Malawi

11.9

5.9

2.9

Guinea Mali

Mauritania

Rwanda

Senegal

Average Transition (%) (of population completing primary education) Benin

Burkina Faso

18.2 14.7

26.3

11.1

16.5

18.2

12.5

12.4

9.6

12.4

3.2

2.6

11.4

5.4

9.6

7.2

10.3

12.5

3.6

Distance: home - lower secondary school

5.6

4.0

0-15 min.

15-30 min.

30-45 min.

45 min. or more

66.7

50.6

50.4

45.8

74.2

62.6

Burundi

46.0

42.6

35.5

Guinea

83.2

66.2

50.5

Mali

63.8

59.5

51.5

Rwanda

57.9

38.0

18.2

Average

69.0

59.1

51.2

45.4

Congo

Malawi

Mauritania Senegal

57.6 73.7 92.1 75.0

57.6

46.5

85.8

56.6

49.6

Source: authors, based on household surveys, QUIBB in particular, which adequately document spatial aspects.

Graph 8. Primary-secondary transition index according to distance between home and school

Source: authors, based on household surveys, QUIBB in particular, which adequately document spatial aspects.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 32

57.6 35.5 42.1 49.6

2. Putting the Lag in Rural Schooling into Perspective

A comparable type of structure is registered when basing

who have completed primary education) on an index of 100

the estimations on the overall relevant school-age

for the situation of the two populations and for the group of

population and not just on the population having completed

those with a school nearby. Graph 9 has been drawn up in

locally (it is probable that the primary school is close by too)

population is below the one for the population that has

primary education. Thus, children with a secondary school

this perspective. The fact that the curve for the total

have an 18.5% chance of accessing secondary education;

completed primary education, suggests that the distance to

this falls to 12.5% if the school is between 15 and 30

the nearest secondary school has on average had an

minutes away and to 5.4% if the school is more than 45

influence before primary completion. This is confirmed by

partly explained by the distance between home and

survival in primary education, were influenced by the

minutes away from the family home. These figures can be

the analysis showing that marginally access, but above all

secondary school for those who have completed primary

chances of being able to pursue studies in secondary

education, but may also be linked to dropouts before the

education. One can indeed expect the proximity of a

primary education.

their families) to stay on at primary school in order to take

end of primary education and/or to survival in the course of

secondary school to be an encouragement for children (and

advantage of the opportunity offered them; if the secondary

To highlight, and separate, what comes into play in the

school is far from home, then parents are not so familiar

and before that point, we can calibrate the two series (the

resulting in a lesser determination on the part of pupils to

transition between primary and lower secondary education

with it and are concerned about the difficulties of access,

one on the overall population and the other limited to those

complete primary education.

Graph 9. Index of access to lower secondary in rural areas according to distance to secondary school for total population and population that completed primary education

Source: authors, based on household surveys, QUIBB in particular, which adequately document spatial aspects.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 33

2. Putting the Lag in Rural Schooling into Perspective

2.2 The Weight of the Rural Environment in Schooling Dynamics in Lower Secondary Education After having observed the lag in schooling for youth living in

secondary enrollments in a given country and the level of

a rural environment compared to their urban counterparts –

development of this level of education. Graph 10 illustrates

and bearing in mind that urban populations are also

this relationship: the lower the overall enrollment rate, the

countries in our sample – we understand that the major part

areas, to the disadvantage of the latter.

generally a minority in the population of the different

greater the schooling disparities between urban and rural

of the efforts to be deployed for lower secondary

development will have a rural accent.

The existence of this overall average relationship, although

This section puts forward a method for estimating the number

of some countries whose position on the graph is far from

very significant, does not do away with the specific situation

of country inhabitants of lower secondary school-age that are

average; this is particularly the case for Ethiopia, Guinea-

not in school. To do so, we shall simply start from a measure of

Bissau and Mozambique, where the level of geographic

household surveys analyzed at the beginning of this study. But

basis of their overall coverage level in lower secondary

those not enrolled. This population will consequently be the

characterized by a lower intensity in geographic disparities

enrollment rates in urban and rural environments taken from the

disparities exceeds what could have been expected on the

unlike the traditional perspective, we explore the situation of

education; in contrast, Rwanda and Tanzania are

target to be reached in coming years in order to move towards

than expected on the basis of their low level of enrollment.

more extensive coverage in secondary education.

This structure of enrollments between urban and rural

Table 11 gives the value of the enrollment rate in lower

areas, and this negative relationship between the intensity

secondary education in the recent period broken down

of disparities and the level of coverage, are coherent with

between urban and rural areas on the one hand, and the

the argument whereby education systems develop

measure of the number of children not enrolled in both

according to a global principle of logistic and social ease.

contexts on the other. This enables an estimation of the

Historically, educational services are firstly developed in

potentially to be enrolled.

cities), where it is relatively easy to set them up due to i) the

proportion of rural youth in the total population still

urban areas (especially in large towns, particularly capital density of population and an adequate number of children

We can see that the average regional enrollment rate of 36% is

coming out of primary school and ii) the strong demand for

distributed between 22.2% for rural populations and 65.6% for

schooling from a well-to-do and influential population. Then,

lower secondary coverage as this ranges from 7% in Niger to

contexts where populations are fewer in number and the

urban populations. Not all countries have the same level of

the services are gradually scaled up in more difficult

76% in Ghana, and urban-rural disparities also differ

demand for schooling lower for these types of services

considerably depending on the country. While the chances of

(small towns, outskirts of towns). Rural areas, especially

enrollment in lower secondary education are on average almost

those with low population densities, are only reached at the

three times (2.9 times) higher for an urban child than for a rural

end of the quantitative expansion process for services. This

Nigeria, Togo and Zimbabwe, but is over 7 in Burkina Faso,

counter-examples, but dynamics of this sort do in general

child, the ratio of urban to rural GER is below two in Ghana,

is of course an overall tendency, no doubt with many small

Guinea-Bissau, Mozambique and Niger.

seem to be at work.17

Not surprisingly, in accordance with the precepts of sociology of education, a negative relationship is observed

We also use United Nations population data in projections dated 2004.17

17 These dynamics no doubt apply to African countries in the current period, just as they did in developed countries 50 or 80 years ago.

between the degree of urban-rural disparities in lower

59.5

41.8

25.1

70.0

19.3

Chad

95.5

18.4

61.8

25.6

76.3

Ghana

38.2

Mali

64.1

Rwanda

11.8

69.4

55.5

36.0

68.9

73.0

65.4

87.5

64.7

78.2

57.8

55.4

85.4

63.7

80.0

61.8

60.4

68.4

84.1

80.6

81.4

58.6

63.8

58.1

64.9

83.7

70.3

49.9

78.5

50.9

40.9

61.8

43.4

66.7

46.5

47.7

33.1

23.9

62.9

58.6

25.9

17.3

28.4

32.7

65.1

41.5

65.6

10.6

31.1

45.3

33.9

42.7

41.3

13.8

25.2

44.3

28.5

28.7

83.6

41.9

35.4

% Rural students

22.2

46.6

31.7

17.5

49.9

8.5

31.2

13.9

8.7

59.0

2.4

8.0

15.3

18.3

24.8

12.4

35.5

2.3

19.6

59.4

14.1

13.1

13.2

8.0

42.2

10.9

12.0

34.9

16.1

9.2

28.4

GER (%) Rural

65.6

77.8

78.3

61.3

99.0

33.6

86.2

54.8

30.0

73.1

26.8

61.9

58.9

81.3

70.6

72.4

81.4

27.4

76.4

99.7

50.7

90.3

44.5

49.9

90.2

50.0

44.4

89.7

67.4

72.8

66.5

GER (%) Urban

Source: compiled by the authors based on household surveys listed in Appendix 1.

Overall

47.8

Zimbabwe

Zambia

23.0

Uganda

14.0

Togo

Tanzania

54.4

32.1

Sierra Leone

Senegal

7.3

Nigeria

Niger

28.6

32.6

32.1

Mozambique

Mauritania

Malawi

44.0

24.0

Madagascar

Lesotho

12.7

40.1

26.9

Guinea-Bissau

Guinea

Gambia

22.5

Ethiopia

DRC

29.0

Côte d’Ivoire

Congo

CAR

Burundi

Cameroon

64.8

79.8

44.0

20.8

% Rural school age pop

GER (%)

Burkina Faso

Benin

Country

49 675.8

1 379.6

600.3

2 463.1

585.8

3 453.7

352.7

1 104.4

711.7

9 715.9

1 183.3

1 249.2

199.6

1 272.7

529.8

1 617.6

146.5

100.4

875.3

1 492.0

90.3

7 316.9

5 432.4

1 828.8

378.3

881.7

387.1

1 530.9

756.9

775.3

1 263.6

Sec 1-. age pop (000)

8 623.8

469.3

124.5

377.2

189.2

229.6

63.5

84.8

53.0

3 649.9

22.3

61.8

18.5

159.0

110.6

161.7

42.3

1.4

109.4

515.3

8.2

801.3

504.1

73.0

66.7

75.5

27.7

272.0

116.1

93.0

142.9

Rural population enrolled (000)

26 692.4

537.8

268.4

1 778.1

190.0

2 471.2

140.2

527.0

554.4

2 534.5

923.8

709.7

102.1

711.5

335.1

1 142.1

76.9

57.5

449.2

351.6

50.4

5 326.3

3 314.9

839.6

91.4

616.2

202.6

507.8

606.9

915.4

359.7

Rural population not enrolled (000)

9 721.7

289.8

162.4

188.7

204.5

253.0

128.4

270.0

31.3

2 582.5

63.6

295.6

46.6

326.9

59.4

227.2

22.2

11.4

242.0

623.4

16.1

1 073.4

718.0

457.2

198.6

95.0

69.6

674.0

22.8

169.7

198.5

Urban population enrolled (000)

4 637.9

82.7

45.0

119.2

2.1

499.9

20.6

222.6

73.0

949.0

173.6

182.0

32.5

75.4

24.7

86.6

5.1

30.1

74.7

1.7

15.6

115.9

895.4

459.0

21.6

95.0

87.2

77.1

11.0

85.5

74.2

Urban population not enrolled (000)

31 330.3

620.5

313.4

1 897.2

1 92.1

2 971.2

160.8

749.6

627.4

3 483.5

1 097.4

891.7

134.6

786.9

359.8

1 228.7

82.0

87.6

523.9

353.3

66.0

5 442.2

4 210.3

1 298.6

113.0

711.2

289.8

585.0

618.0

1 000.9

433.8

Total population not enrolled (000)

84.7

86.7

85.6

93.7

98.9

83.2

87.2

70.3

88.4

72.8

84.2

79.6

75.8

90.4

93.1

93.0

93.8

65.6

85.7

99.5

76.3

97.9

78.7

64.7

80.9

86.6

69.9

86.8

98.2

82.9

91.5

% Rural in population not enrolled

Table 11. Gross enrollment rate in lower secondary education according to geographic environment and estimation of the weight of the rural context in populations still to be enrolled at this level

2. Putting the Lag in Rural Schooling into Perspective

Graph 10. Scale of urban-rural disparities according to level of enrollment (end of lower secondary)

Source: compiled by the authors based on household surveys listed in Appendix 1.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 36

2. Putting the Lag in Rural Schooling into Perspective

Diagram 1. Proportion of population in and out of school in urban and rural areas

Rural areas

Urban areas

The left-hand column in the diagram represents the rural

urban and rural areas in the household surveys mobilized in

(68.9% on average of the country’s total population of the

enrolled in this age group in urban and rural areas in these

population of lower secondary school age in a given country

this study, and iii) the proportion of children enrolled and not

same age group in the sample countries), while the right-

same surveys. This information is given in Table 11. We can

hand column represents the corresponding urban

then estimate the proportion of young people from rural

grey) represents the sub-population actually enrolled in

in fact not enrolled in lower secondary education.

population (31.1%). The lower part of the diagram (light

environments in the young population as a whole who are

lower secondary education in the recent period in each of

the two populations (representing on average 22.2% of the

As an aggregate value for the sample countries as a whole,

rural population and 65.6% of the urban population), while

it is estimated that there are 49.7 million young people of

the sub-population not enrolled (77.8% of the rural

million in rural areas and 9.7 million in urban areas) are

on the other hand the upper part (cross-hatch) represents

lower secondary school age, of whom 18.3 million (8.6

reference population and 34.4% of the urban population

actually enrolled, while 31.3 million children are not. 26.7

respectively in an average country in our sample).

million of the latter live in a rural environment and 4.6 in an

The number of individuals in the different populations

enrolled, in the population of lower secondary age, reaches

urban environment. The share of rural children not actually

featured in the figure above can be identified for each

the extremely high figure of 84.7%. The conclusion

country on the basis of i) the estimated total population of

whereby the challenge of expanding lower secondary

lower secondary school age in each of the countries (United

education coverage – the point where the impact of growing

Nations data), ii) the distribution of this population between

pressure on post-primary education is initially felt – is first of

2. Putting the Lag in Rural Schooling into Perspective

all an issue of educational services in rural areas could not

however from one country to another. Thus, the share of

be better illustrated (since this is indeed the population to

rural children in the population not enrolled in lower

goals).

Central African Republic, Côte d’Ivoire, Nigeria and

This situation is valid qualitatively for all the low-income

Ethiopia, Ghana, Lesotho, Madagascar, Malawi, Mali,

be included in order to achieve the targeted quantitative

secondary education is around 70% in countries such as Senegal, while it is over 90% in Burkina Faso, Burundi,

countries in Sub-Saharan Africa; it may vary to some extent

Uganda and Togo.

3. Lower Secondary Educational Services in Rural Areas: Variability across Countries and Leeway for Development

3.1 Specificity of the Organization of Lower Secondary Educational Services in Rural Areas We have seen that, in the recent period, rural youth has far

generic and not intended to precisely represent a specific

lower access to secondary education than urban youth, on

country, illustrate the type of situation encountered.

average in the countries in the region. We have also

identified that most of the progress to be made in lower

The graph shows the overall amount of current resources

secondary enrollments concerns rural areas. We shall now

that need to be mobilized for each school compared to the

examine some aspects of the services offered in rural

number of enrollments. It is the average relation between

areas, and more particularly the organizational and

the two orders of magnitude that is represented on the

financial aspects. Indeed, it is obvious that, in the present

graph. Both the gradient and the intercept point are seen to

situation, there are considerable differences between urban

be positive. Connecting the origin of the axes and any point

identifying educational services in rural areas that would be

directly provides, through the gradient of this straight dotted

affordable unit costs.

of the volume of resources to the number of students. It is

and rural schools, and implicitly the issue arises of

of the estimated relation (straight dotted lines on the graph)

both effective and of suitable quality and also have

line, the unit cost (expenditure per student), that is, the ratio

then quite clear that the expenditure per student is higher

In order to explore this issue, we take the analysis to the

for a small school A than for a larger school B.

level of individual lower secondary schools, beginning with

a description of the resources actually granted to them according to the number of students enrolled and the

implicit or explicit arrangements existing in the different

countries.

Generally speaking, it is expected that, on average, schools

with a higher number of enrollments will benefit from more

resources in order to fulfill their education mission, and therefore that there will be an ascending relationship

between resources mobilized and the number of

18 We have also observed that this situation was to a great extent to do with the fact that rural children have access to primary school less frequently than urban children and that, when they do have access, they are subject to more frequent dropping out than children in towns. The consequence of these two phenomena is that a much lower proportion of rural children complete primary education compared to urban children. On this basis, the chances of transition to secondary education for those completing primary education are indeed somewhat lower in the country than in towns, but the difference is not considerable.

enrollments.19 The analyses conducted show that while this

relationship is generally more or less linear, it is also characterized by an intercept point that is positive,

demonstrating the existence of economies of scale in

19 We do not tackle the issue of the random factor in the distribution around the average relation here concerning the management of the system (and which obviously deserves to be analyzed as such); it is the average relation concerning the educational policy that we are to focus on here.

school production and a cost function implying higher unit costs in small schools. The two graphs below, which are

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 39

3. Lower Secondary Educational Services in Rural Areas: Variability across Countries and Leeway for Development

School resources

Graph 11. Amounts of resources of a lower secondary school by number of students

Source: compiled by the authors.

Number of students

Unit cost

Graph 12. Unit cost by number of students in the school

Source: compiled by the authors using Graph 11.

Number of students

This is more immediately visualized in Graph 12 (derived

a school size of over 300 students (in the hypothetical

numerical value of the unit cost according to the number of

extra cost in small schools and the reference size T

from Graph 11), which directly shows the average

example). This therefore highlights both the intensity of the

enrollments in the school. The relation is globally

(sometimes called optimum size) above which the unit cost

descending with a higher level of expenditure per student in

no longer significantly diminishes as school size increases

small schools, tending to stabilize more or less starting from

(the idea that the level of expenditure per student will only

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 40

3. Lower Secondary Educational Services in Rural Areas: Variability across Countries and Leeway for Development

fall by 10% once the size has increased from T* to 800 students).

Concerning curriculum definition first of all, the

distinction can be made between a quantitative dimension and a distributive dimension. The quantitative

Graphs 11 and 12 have of course been built by way of

dimension measures the number of teaching hours

illustration, primarily with a view to explaining the method

scheduled for the students, for example 28 hours per

used. The working hypothesis is that while the generic form

week, 32 weeks in the year. The distributive dimension

no doubt applies fairly well to the situation of each of the

measures the way in which these hours are distributed

target countries in our study, the precise form can also

across the different subjects, the number of subjects (6

significantly differ from one country to another. For

subjects for example) on the one hand, and the number

between A and B type schools (Graph 11) is 3 in certain

teaching language, 5 hours for maths, 3 hours for sci-

example, it is possible that in reality, the ratio of unit costs

of hours allocated to each subject (6 hours to the

countries but only 1.4 in others. Similarly, it is possible that

ence…) on the other.

the optimum size T may be estimated at 250 students in a

given country and at 400 students in another. Before

Concerning teachers (those who have a direct in-

class teaching function) secondly, it is also relevant to

tackling the empirical analyses, it may be of use to start by

distinguish a quantitative dimension and a distributive

asking oneself the following questions: i) why do structures

dimension. The quantitative dimension is linked to their

education and ii) why would they differ from one country to

supposed to complete in the course of the week (20

of the type described above exist in lower secondary

status and to the number of hours of service they are

another?

hours for example). The distributive dimension is linked

to the teachers’ degree of polyvalence/specialization;

To explain the existence of economies of scale and of

for example, teacher training may target a single sub-

higher unit costs in small schools, two aspects may be more

ject (maths for example) or rather several subjects on

particularly taken into consideration:

administrative,

It is clear that, in a given country, i) the longer the teaching

minimum non-teaching staff once a school is

of subjects to be taught, iii) the shorter the teachers’

the

first

the

existence

the curriculum (both maths and science, for example).

pedagogical or technical personnel making up the

hours scheduled for the students, ii) the higher the number

operational (bearing in mind that some of the personnel

number of hours of service, and iv) the more specialized the

may have teacher status, but it is the function that is

teachers are at subject level, the more we can expect to

important here). As the minimum team exists whether the

need firstly a large number of teachers for the school to be

related financial salary charges constitute a fixed

teachers, independent of the size of the school, in order to

being necessarily distributed over the number of

to the notion of a fixed salary cost component of education,

school has for example 80, 150 or 200 students,

the

operational and secondly a high minimum number of

component of the cost. The corresponding amounts

transmit the scheduled curriculum. We therefore come back which is added to the fixed cost of other non-teaching staff

enrollments in the school, it ensues that they have a

as identified earlier.

higher quantitative impact on the unit cost when the number of enrollments is lower.

On these different points, there may potentially be

second aspect concerns teaching personnel.

substantial variations from one country to another, which

statutory rules governing their profession, are there to

education from country to country and, secondly, affect,

the

Teachers, with the training they have received and the

will in the first place affect the average level of unit cost of

provide the teachings set out in the curriculum definition

adopted by the administrative division for the given cycle

20 This is not contradictory with the possibility of the structure becoming more consequent when considering larger schools.

of studies.

3. Lower Secondary Educational Services in Rural Areas: Variability across Countries and Leeway for Development

possibly significantly, the level of extra costs for small (rural)

Beyond the distinct numerical value of these two statistics, the

schools compared to bigger schools that are usually in an

ratio between the two is also meaningful, since this is what

organizational aspects are not legion; some estimations on

of the extra cost of small schools. On average for the region,

urban setting. The empirical information on these

most directly influences the overall unit cost and the intensity

the time aspect are however available.21

this ratio is 1.56, demonstrating that there is, in a typical SubSaharan African country, a little over one and a half teachers

Concerning the official teaching time for students per week,

per class in lower secondary education. However, this ratio

the average regional value is estimated at 27.5 hours (see

varies considerably between countries; while it is under 1.3 in

hours or less in Central African Republic, Congo, Ethiopia,

Niger and Zambia, it reaches 1.8 or more in Benin, Burundi,

Table 12) but there is some variation; this ranges from 25

the Democratic Republic of Congo, Ghana, Mozambique,

Gambia and Sierra Leone to 30 hours per week or more in

Guinea, Lesotho, Malawi, Mauritania and Uganda.

Rwanda and Togo. The average number of hours actually

Aside from the aspects of students’ and teachers’ school

another on account of the statutory hours that vary between

each country regarding the distribution of teaching time per

actually apply them.22 Statistics show an average regional

specialization in teacher training on the other; unfortunately,

Benin, Burkina Faso, Burundi, Lesotho, Madagascar, put in by the teachers also varies from one country to

hours, it would be important to document the situation in

countries and to the difference in the countries’ capacity to

value of 18.1 hours per week,

subject on the one hand and the degree of subject

with the values in the

it was not possible for us to collect this information at this

different countries ranging from 15 hours or less in Gambia,

stage of the study. That is why we are now to directly

Guinea, Malawi, Mauritania, Sierra Leone and Uganda to

examine the extent of economies of scale (that result from

Congo and Niger, and even 25 hours or more in the case of

countries where the information could be mobilized and the

20 hours or over in Cameroon, Democratic Republic of

the influence of these factors) empirically, in the sample of

Ghana and Mozambique.

3.2

analysis conducted.

A Significant Structure of Economies of Scale

We were able to collect the school census files for

(but all the same we do have indications for 21 countries in

secondary education in twenty-one countries for recent

the region); it is not totally homogeneous either since non-

years, and target schools offering only lower secondary education.

teaching staff could not be identified in some countries

These countries are: Benin, Burkina Faso,

while in others they could. Table 12 presents the data

Burundi, Cameroon, Central African Republic, Chad,

collected and the results of the analyses the data gave rise

Ethiopia, Ghana, Guinea, Lesotho, Madagascar, Malawi,

to. It provides some interesting information.

Mali, Mauritania, Mozambique, Niger, Republic of the

first concerns the average lower secondary school

Congo, Rwanda, Senegal, Togo and Uganda. The

The

21 It is to be noted that the figures concerning student and teacher time are estimated data and so subject to some approximation; this does not alter the fact that there is most likely a noteworthy variability between countries on these two aspects, as noted hereafter.

23 This figure corresponds to normal operation of the system. This time, which we associate with the actual amount of time dedicated to work, is in fact overestimated since it does not take into account the fact that the school year can begin late or end early, or yet again that there may be some teacher absenteeism in the course of the ordinary operation of the services.

size in the recent period and the proportion of small

information is therefore not complete on a regional scale

22 The number of hours is known to vary within the country from one school to another under the joint influence of the random factor in teacher allocation to individual schools (when there is over-allocation in some – usually urban – schools, all teachers are not fully employed) and difficulties in making full use of teachers in small schools.

24 We have particularly not taken into account the case of the generally large urban schools that provide both lower and upper secondary education.

3. Lower Secondary Educational Services in Rural Areas: Variability across Countries and Leeway for Development

schools (those in rural areas, bearing in mind that the

school supply side, since it involves the existence of

censuses). The average number of students per school in

plicated to organize due to teacher specialization in

many smaller schools; it is also potentially more com-

urban-rural distinction is rarely available in school

lower secondary education with probably negative con-

our sample is 494. But this average corresponds to much

sequences on schooling costs (we shall examine this

lower figures in Ghana (124 students) and the Republic

point later in this paper). However, it does have obvious

of the Congo (146 students), and high figures in Benin

(962), Central African Republic (1 313) and Ethiopia (1

advantages from the demand angle due to the geo-

countries on the statistic concerning the percentage of

compared in spirit to primary school and, in this

graphic proximity of the schools. This option can be

343). There is also considerable diversity between

respect, undoubtedly contributes to providing basic

schools with fewer than 250 students (indeed a

education bringing together de facto the traditional cat-

conventional reference) with very low figures in Benin

egories of primary education and lower secondary edu-

(13%) and Ethiopia (11%), while the corresponding

cation.

figures are 81% in Congo and 91% in Ghana for a regional average of 46%.

The

second piece of information provided in Table 12 is

that, as we can obviously expect, in each country the

It is likely that this diversity can partly be understood by the

number of staff (teachers and/or non-teachers) assigned

diversity of the overall geographic distribution of the

population in the different countries on the one hand and to

to a lower secondary school is related to the number of

secondary enrollments on the other; but it is likely that this

substantially from one country to another:

students enrolled there. However, this ratio varies most

the proportion of urban enrollments in overall lower

diversity is also to do with the different strategies adopted

Firstly, it differs in intensity. While it is obviously

by the countries in terms of “school mapping” in the broad

desirable for schools with more students to dispose of

grouping students together from a relatively extensive

same number of students to benefit from more or less

more resources, it is also desirable for schools with the

sense of the term. While some countries have opted for

comparable numbers of teachers and non-teaching

geographic area, others have clearly opted for local schools

staff; yet this is never exactly the case and it is interest-

(the reality may be somewhere between these two

ing to observe that the value of the random factor [1-R²]

strategies).

in personnel allocation varies considerably from coun-

The first option is obviously favorable on the

try to country.25 The average value of the random factor

supply side because this means there are few large

is estimated at 34% (a higher figure than the 27%

schools. This makes management easier to handle

noted in primary education using comparable method-

and makes it possible to better control unit costs; but

ology), but while it is under 20% in Chad, Guinea and

it is unfavorable on the demand side, as we have

Togo, it exceeds 50% (and so corresponds to a quality

seen that distance represents an obstacle to educa-

of personnel management that definitely needs to be

tion, particularly for the least privileged categories.

improved upon) in Burkina Faso, Burundi, Cameroon,

When the scaling-up process of lower secondary

Congo and Mali.

enrollments begins, with principally urban schooling

and low rural coverage, the disadvantages of this

option are obviously limited; but these disadvan-

tages are to become more and more obvious as

progress is made towards broader coverage of the

system; this option will in fact become less and less sustainable.

25 The numerical value of the random factor is a measure of a country’s capacity to effectively and fairly allocate personnel to schools. A value of zero would identify “perfection”, and so a perfect quality of personnel allocation management.

The second option is more difficult a priori on the

Teachers

Chad

Teachers

Gambia

Ghana

Guinea

Guinea-Bissau

Lesotho

Madagascar

3.62

3.89

0.0228

0.0221

0.0268

0.0154

0.0126

0.0333

0.0128

0.0156

0.0244

0.0198

0.0173

0.0135

0.0171

0.0471

0.0277

0.0196

0.0166

0.0127

0.28

0.26

0.35

0.20

0.19

0.31

0.23

0.22

0.11

0.12

0.23

0.26

0.59

0.83

0.65

0.60

0.26

0.60

273

390

644

124

1 343

313

1 313

386

292

290

962

13.3

Schools Average %< 250 enrollments students

120/500* = ratio comprising student-staff ratios for 120-student schools and 500-student schools.

Personnel

3.13

5.16

Personnel

2.53

3.82

6.15

8.33

Personnel

Teachers

Personnel

Teachers

Personnel

Ethiopia

Eritrea

Rep. Congo

Democratic

Personnel

Teachers

3.14

Personnel

2.02

5.41

3.50

Personnel

Côte d'Ivoire

Republic

Teachers

5.20

Personnel

Central African

8.18

Teachers

Cameroon

2.65

Personnel

Teachers

Burundi

2.55

Personnel

0.80

4.75

Teachers

Burkina Faso

Teacher-student ratio Constant Coefficient Random [1-R²]

Personnel

Teachers

Type of personnel

Benin

Country

Table 12. Lower secondary schools: the extra cost of small schools

5.44

5.66

5.27

6.40

4.82

5.19

7.17

9.58

5.09

3.61

6.79

4.58

6.57

11.95

4.87

4.12

6.08

1.81

6.35

6.54

6.35

7.01

5.33

6.53

7.68

10.20

6.07

4.40

7.49

5.12

7.25

13.83

5.98

4.90

6.74

2.32

120

7.04

7.21

7.15

7.47

5.71

7.53

8.07

10.67

6.80

4.99

8.01

5.53

7.76

15.24

6.81

5.49

7.24

2.70

12.74

12.73

13.85

11.32

8.86

15.85

11.27 14.57

12.90

9.94

12.33

8.80

12.04

27.02

13.73

10.39

5.88

11.39

15.02

14.94

16.53

12.86

10.12

19.18

12.55 16.13

15.34

11.92

14.06

10.25

13.75

31.73

16.50

12.35

7.15

13.05

1.76

1.82

1.60

2.27

2.19

1.42

2.55 2.64

1.65

1.54

2.22

2.08

2.20

1.82

1.51

1.65

2.15

1.35

Enrollments per school 150 400 500 120/500*

32.0

30.0

25.0

28.0

25.0

18.7

24.0

25.0

28.0

25.0

28.0

35.0

30.0

32.0

Student hours

19.8

16.0

18.6

15.0

25.0

11.2

17.1

19.0

24.3

16.5

19.1

18.3

21.6

16.9

20.0

18.0

Hours per week Teacher hours

1.62

1.88

1.34

1.87

1.00

1.67

1.40

1.32

1.15

1.70

1.47

1.37

1.30

2.07

1.50

1.78

Stud.Hrs/ Teach.Hrs

3. Lower Secondary Educational Services in Rural Areas: Variability across Countries and Leeway for Development

Teachers

Rwanda

Teachers

Sierra Leone

Tanzania

Togo

Uganda

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010

Zambia

Overall

4.14

2.88

0.027

0.023

0.053

0.033

0.0146

0.0136

0.0237

0.0380

0.0541

0.0294

0.0383

0.0298

0.0244

0.0281

0.0174

0.0148

0.0354

0.25

0.34

0.35

0.24

0.18

0.17

0.22

0.39

0.43

0.49

0.27

0.28

0.50

0.51

0.32

494.0

409

493

588

526

146

451

439

299

200

6.3

4.7

14.31

9.32

2.03

1.23

5.39

5.02

7.59

4.80

3.53

2.78

2.70

6.35

4.09

4.03

5.61

Source: compiled by the authors using the secondary school statistics database for the different countries.

Personnel

10.07

Personnel

6.68

0.86

0.14

3.49

1.98

3.26

2.45

Personnel

Teachers

Personnel

Senegal

the Congo

0.47

Teachers

Republic of

0.39

Personnel

Teachers

Niger

0.75

Personnel

Teachers

Mozambique

4.10

Personnel

Teachers

Mauritania

2.70

2.85

2.78

Personnel

Teachers

Mali

Personnel

Teachers

Malawi

2.61

7.4

5.6

16.43

10.64

1.77

6.34

6.54

9.76

5.98

5.06

3.97

3.67

7.47

4.79

4.63

7.02

3.05

8.2

6.3

18.02

11.63

2.18

7.05

7.68

11.38

6.86

6.21

4.86

4.41

8.32

5.31

5.07

8.09

6.70

14.9

12.0

31.27

19.88

5.58

12.97

17.18

24.90

14.21

15.79

12.31

10.51

15.34

9.66

8.77

16.94

8.16

17.6

14.3

36.57

23.18

6.94

15.34

20.98

30.31

17.15

19.62

15.29

12.95

18.15

11.40

10.25

20.48

1.33

1.76

1.65

1.87

1.91

1.06

1.72

1.30

1.34

1.45

1.08

1.18

1.72

1.75

1.88

1.43

27.6

25.0

27.3

30.0

26.7

23.3

28.0

30.8

25.0

26.0

30.0

28.0

18.3

19.2

12.2

18.5

16.9

14.0

18.0

19.0

18.6

21.0

28.2

15.0

17.7

15.0

1.56

1.30

2.24

1.62

1.58

1.66

1.56

1.62

1.34

1.24

1.06

2.00

1.58

1.87

3. Lower Secondary Educational Services in Rural Areas: Variability across Countries and Leeway for Development

This relation also differs in the numerical values of

non-teaching staff), this may only register at around 10

of enrollments. By way of illustration and on the basis

Republic of the Congo and Uganda; these differences

its coefficients, both for the constant and the coefficient

in Mali and Togo and reach over 30 in Burundi,

of the analysis of the personnel as a whole, the

are obviously considerable.

constant can be very close to zero as in Niger and Togo

10 in Uganda; such variations are also observed in the

sion of this study, as to the level of extra cost inherent in

interest to us here on account of the urban-rural dimen-

or be as high as 8 as in Burundi and Ethiopia and even

small (rural) schools in reference to larger (urban)

value of the coefficient of enrollments. These variations

coefficients

show

differences

that

can

Finally, this relation differs, and this is of particular

schools. For that, we use the relation between the per-

sonnel (total staff or only teachers) and the size of the

considerable in the human resources employed in a

school. Through simulation, we can estimate the staff

school of a given size depending upon the country

numbers corresponding to different school sizes. Graph

where it is established. For example, in the case of an

13 below shows the indicator provided by the average

urban lower secondary school, with 500 students, then

student-staff (teachers and total staff) ratio for all the

it is observed that while the average for the sample

countries, according to the number of school enrollments.

countries is for 18 members of staff (14 teachers and 4

Number of students per staff member

Graph 13. Student-staff ratio according to the number of school staff (average African country)

Teachers

Personnel

Number of students Source: compiled by the authors using secondary school statistics base for the different countries.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 46

3. Lower Secondary Educational Services in Rural Areas: Variability across Countries and Leeway for Development

Unit salary cost (USS)

Graph 14. Unit salary cost (US$) according to the number of students in the school (average African country)

Personnel

Teachers

Number of students Source: compiled by the authors using secondary school statistics base for the different countries.

It is clear that the student-staff ratio is more favorable when

In rural areas, for reasons of both effectiveness and

teacher (20 students per staff member) for a school with

small, schools to be able to operate and provide good

and 38 respectively for a 600-student school. These gaps

as a constraint, there are no doubt potentially different

are better supervised quantitatively in small schools, this

encountered in the different countries sheds some light

the school is smaller, with an average of 14 students per

equity, it is thus important for small, and sometimes very

only 100 students, while the corresponding figures are 29

quality educational services. Although this may be seen

necessarily have financial implications as, while students

ways of achieving it. Analysis of the situations

leads to higher unit costs of schooling. Graph 14 provides a

on this point. It is indeed possible that some countries

low-income Sub-Saharan African country.

organization of their education system as far as

measure of the unit salary cost in US dollars in an average

might have adopted better formulas than others for the

managing the issue of small schools is concerned and

This relation is of the same type anticipated in the initial

that this may be apparent in the analysis of their unit cost

analysis framework with the unit cost of schooling

function. On the basis of an estimation of the relations of

while enrollments are limited and stabilizing progressively

country we calculate the ratio of the unit cost for an

decreasing as school size increases, significantly at first

the type represented in Graphs 11 and 12, for each

when considering large schools. It is difficult to determine a

average 120-student school (a more or less average size

be economies of scale, but visually this point is situated

school (a more or less typical urban school). On average

precise enrollment size above which there would no longer

in rural areas) to that of a 500-student lower secondary

somewhere between 350 and 500 students (the relative

for the countries where this analysis was conducted as a

difference in average unit cost for these two school sizes is

whole, this ratio is 1.65 when targeting teachers alone,

preferable for all schools to enroll at least 350 students. In

teachers alike.

possible when there is a low density of population or when

The curves on both graphs above (13 and 14) correspond

only 6%). On a strictly economic level, it would of course be

and 1.76 when considering both teachers and non-

urban areas, this is quite feasible; but it is generally not

local populations comprise less than 4 500 inhabitants.

to an average situation for the countries as a whole, and

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 47

3. Lower Secondary Educational Services in Rural Areas: Variability across Countries and Leeway for Development

this average may conceal noteworthy differences from one

In each of the five countries we observe a comparable relation

country situations by comparing the average relationship

a salary unit cost that decreases depending on the school

country to another. Graph 15 illustrates these differences in

between the two orders of magnitude taken into account with

between total salary unit cost (all personnel: teachers and

size; this general pattern is observed in all countries. But there

non-teachers) and the number of enrollments in a school in

are however some very significant differences:

a selection of countries (Burundi, Congo, Ethiopia, Madagascar and Togo).

The actual differences in salary unit cost between

countries (around 120 dollars), but at the same time

In Burundi and Congo, the salary unit cost of a

600-student school26 is seen to be similar in the two

this is significantly higher (nearly double) than what is

countries are to do with both i) the number of personnel

observed on average in the region (64 dollars). But

(teachers/non-teachers) employed in the schools which

Burundi and Congo are also seen to differ somewhat

may vary depending on the size of the school; ii) the

level of salary (expressed in dollars or in units of GDP

when comparing salary unit cost in small schools.

ways in which schooling is organized and personnel

above the regional average for small schools. Thus, for

However, in both these countries, this is still very much

per capita). As we are targeting more particularly the

an intake capacity of 100 students, the unit cost is

utilized, we have assigned all countries the average level

estimated at 258 dollars in Burundi and 173 dollars in

of salary observed in an average African country; this

does away with the salary level dimension in the

Congo compared to a regional average of 125 dollars.

consequences

school than in a 600-student school in Congo, and

The unit cost is therefore 46% higher in a 100-student

comparison, making it possible to focus on the of

the

different

organization and management.

types

school

112% higher in Burundi.

Graph 15. Relationship between total salary unit cost and number of students in a selection of countries

Burundi Congo

Unit cost (USS)

Ethiopia

Madagascar Togo

Overall

Uganda

Number of students

Source: compiled by the authors using secondary school statistics base for the different countries.

Salary unit costs are in a way conceptual insofar as a reference salary that is common to all the countries has been applied to all personnel (regional average) in order to do away with the influence of variety in the levels of salary between the different countries. The said variety, although very much a reality, would have interfered with our comparisons focused on the way schooling is organized and human resources managed in schools.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 48

3. Lower Secondary Educational Services in Rural Areas: Variability across Countries and Leeway for Development

In the case of Ethiopia, we observe unit salary

the relative extra costs between a 100-student school

costs for 600-student schools that are very much lower

and a 600-student school are similar (45 and 46%

than those observed in Burundi and Congo (57 dollars

respectively).

in Ethiopia – close to the regional average – compared

to 120 dollars in the other two countries). But if we

enrollment size of 100 students, then Ethiopia is far

but above all by very high salary unit costs (the highest

greater economies of scale than the regional average,

examine the estimated extra cost for a school with an

in all 21 sample countries), whatever the size of the

from the average regional structure with a very high

school; thus for a 600-student school, the (conceptual)

unit cost (197 dollars). The unit salary cost of a 100-

unit salary cost is estimated at 140 dollars in Uganda,

student school thus exceeds that of a 600-student

while it amounts to only 58 dollars in Madagascar and

school by 235%.

32 dollars in Togo.

Madagascar is very close to the regional average,

whereas Togo is characterized by a much lower

Beyond the differences in unit costs, which can be related

enrollment size of 600 students, the unit salary cost (32

expenditure other than salaries, low-income Sub-Saharan

for this type of school (64 dollars), while the extra cost

organized and managed at lower secondary level (student-

position on the graph. If we look at schools with an

both to differences in salary levels and to resources for

dollars) is very much below (half) the regional average

African countries also differ in the way schools are

for a 100-student school (compared to 600 students)

staff ratios, management of staff tasks). Some countries are

amounts to only 45% (46 dollars in Togo compared to

more effective than others in their capacity to run small

125 dollars for the regional average). Unit costs in

schools at reasonable costs, with the extra cost for a 120-

Congo and Togo are extremely different (the unit cost

student school compared to a 500-student school (Table

registering at almost 4 times more than in Togo), but

70%.

of lower secondary education in Republic of the Congo

3.3

Finally, Uganda is characterized by somewhat

12) varying from 8 to 150% for a regional average of around

An Illustrative Simulation

The previous analysis has shown that, beyond general

this is why we have preferred a simulation approach that

differences in unit costs of schooling in lower secondary

introduces simple variations in organization modes on the

education, the different countries do not all succeed in the

basis of a hypothetical reference structure. This makes it

same way in managing the situation of small schools. In

possible to illustrate the different possible choices for

and target the second (specific management of small rural

they have on unit costs.

order to control the influence of the first (general) dimension

managing small lower secondary schools and the impact

schools, in reference to larger urban schools), the ratio of unit cost in a school with 120 students to another with 500

We have started from the initial and hypothetical situation

these small schools.

second with 500; these two schools apply the same hours

has been taken as an indicator of the capacity to manage

of two schools, the first with 120 enrollments and the

and curricula (28 hours of teaching per week distributed

But, if the ratio of unit costs between these two types of

across the different subjects on the official curriculum in

schools differs from one country to another, it implies that

the hypothetical country on the basis of an established

there are different types of organization in the different

schedule) and use the same teachers in terms of status

countries. As we have already mentioned, we only have a

and salary (18 hours of statutory service and an average

very incomplete description of these organization modes;

annual salary of 1 000 monetary units) on the other.

3. Lower Secondary Educational Services in Rural Areas: Variability across Countries and Leeway for Development

Diverse ways of organizing teacher utilization and

ii) the second consists in using the “available” hours of a

definition of subjects taught are examined without making

teacher within the limit of his/her statutory service time, in

any change to the overall number of teaching hours to

order to contribute to the general running and

students. Table A2 in the appendix details the different

organization of the school (teacher coordination,

configurations simulated; Table 13 provides a synthetic

pedagogical follow-up, contacts with the parents…). This

summary of same.

would lower the school’s needs for full-time non-teaching

The first two lines in the table characterize a hypothetical

penalizes small schools;

staff, which is a “fixed” element in the cost function that

but probable initial situation for the two schools providing a

four-year cycle of secondary education in a context where

iii) the third consists in grouping relatively close subjects27

the subjects on the curriculum can only be taught by a

such as mathematics and science, or else history and

where no overtime is paid over and above the statutory

on the basis of these groups.

geography together, and organizing teacher training plans

teacher who has been specifically trained for that and service (18 hours).

The first simulation introduces the possibility of

The first school has 500 students across four levels and ten

overtime for teachers and the use of “available” hours

prevailing in the country, this school benefits from 21

the number of teachers necessary from ten to eight,

for supervision and administration. This would reduce

pedagogical groups. With the organization modes

increase the student/teacher ratio from 12 to 15, the

teachers (leading to a student-teacher ratio of 23.8) and 3

teacher utilization rate from 0.62 to 0.78, and would

non-teaching staff; the teacher utilization rate is estimated

reduce the number of non-teaching personnel from two

at 74% and the unit cost at 48 monetary units;

to one. 12 hours overtime is paid (25% over regular

hours), but the unit cost falls from 100 in the initial

The second school has 120 students and four pedagogical

configuration to 81.9, representing a saving of 18%.

groups. It benefits from ten teachers (leading to a studentteacher ratio of 12.0) and two non-teaching staff; the

teacher utilization rate is estimated at 62% and the unit cost

at 100 monetary units, i.e. just over twice that of the 500student school.

The purpose of the following simulations is to examine how the small school could reduce its unit cost. Generally

speaking, the goal is to reduce the constraint of strong

inflexibility (non-divisibility) in school organization modes prevailing in the initial scenario, which particularly

penalizes small (rural) schools. Three possible strategies are proposed in this respect:

i) the first consists in authorizing overtime for teachers so as

to avoid recruiting an extra teacher when a fraction of

27 Lower secondary education is situated between primary education, where it is considered preferable to have general teachers (teaching all subjects), and upper secondary education where teachers are specialized in the subjects taught. Some degree of polyvalence and specificity can be justified between the two; monographic studies on this aspect suggest that this formula is satisfactory from the angle of student learning achievements.

weekly teaching service would suffice; in the simulations,

overtime is paid with a bonus of 25% compared to regular

hours of service;

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010

120

Pedagogical Student groups hours / week

Source: compiled by the authors using Table A2 in Appendix 2.

overtime

grouping/polyvalence and

3. With subject

polyvalence without overtime

2. With subject grouping and

120

and without subject grouping

subjects

1. With overtime

500

120

overtime or grouping of

Basic situation without

Enroll -ments

112

280

Teaching hours / week 18

Basic service / week

Table 13. Unit cost connected to different types of organization in a 120-student lower secondary school

1.04

0.78

0.74

0.62

Number of % utilization teachers of teachers

Available

Hours

Overtime

20.0

15.0

23.8

12.0

STR 3

Number of non-teaching staff 48.0

66.4

75.0

81.9

100.0

1.38

1.56

1.71

2.08

Unit Ratio salary 120/500 cost

3. Lower Secondary Educational Services in Rural Areas: Variability across Countries and Leeway for Development

Simulation 2 does not take the elements of the first

at 20 (compared to 12 in the initial situation for the 120-

grouping. The consequences are quite close to those in

The overall teacher utilization rate is 1.4 and the unit

simulation into account but introduces subject

student school and to 23.8 for the 500-student school).

the previous simulation (eight teachers with a utilization

cost 66.4 (one third lower than the initial situation).

rate of 0.78 and 1 non-teaching staff member), but the unit cost, at 75 monetary units, is lower than that in

These are only illustrative simulations, but they do show the

simulation 1 (81.9).

potential existence of options that would lead to controlling

the cost for small schools. There may of course be other

Simulation 3 combines the elements introduced

possibilities, particularly if the number of teaching hours to

respectively in the two earlier simulations, enhancing

students and the number of hours of teacher service are

the benefits of the two formulas. The 120-student

modified simultaneously to make them equal (24 hours per

school can then operate in good conditions but with

week for example). We can arrive in this way at a number

compared to ten and two respectively in the initial

the number of pedagogical groups (i.e. four in the case

only six teachers and one non-teaching staff member

of teachers (without recourse to overtime) that is equal to

configuration. The student/teacher ratio now registers

studied for a school of 120 students).

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 52

The Social and Economic Relevance of Education in Rural Areas

In this section our analysis focuses on the effects and

better participate in organized collective life and make

performance of those who have been trained once they

(demographic growth is controlled better in more

impacts of education in rural areas. We look at the

better-informed

have left the realm of education and have embarked on

political

choices)

demography

educated societies). The connections between education

their social and productive adult life. At individual level,

of the population on the one side, and employment and

we endeavor to determine if what young people learnt in

economic growth on the other, are obviously of prime

school did indeed constitute a good preparation for a

importance. This impact, in both the economic and social

distinguish the social and economic effects on the one

educated individual can earn more than a less educated

favorable economic and social life. In this perspective, we

spheres, can be observed both at individual level (a more

hand from the individual and collective effects on the

individual) and at the level of society (more educated

other. Social effects concern dimensions such as

societies enjoy higher economic growth, and obtain

mortality, health, civic life (more educated individuals can

4.1

better performances in health indicators).

The Social Impacts of Education in Rural Areas 5. Impact of education on maternal health (prenatal check-

Education has effects on a large number of social areas.

This may concern practices (use of a method of

ups, anti-tetanus vaccination, childbirth with skilled

contraception or immunization of children for example) and

attendants, postnatal check-ups, vitamin A intake),

6. Impact of education on child health (vaccination, vitamin

results (number of children in the household or risk of child

mortality before the age of 5). We are in fact constrained by

A intake, risk of child mortality).

the availability of data in household surveys, but these

generally offer observations in a fairly wide range of

As far as this general presentation is concerned, two points

observed situations. The choices we have made are

are to be highlighted:

divided into six major areas, each of which may possibly be the subject of several indicators:

1. Impact of education on reducing the risks of poverty,

look into the impact of education without giving the

the first is that the above issues cannot be

addressed in a generic manner. One cannot directly

2. Impact of education on sustainable literacy in adulthood,

research a marginalist perspective. It is possible to

3. Impact of education on childrenâ&#x20AC;&#x2122;s schooling, particularly

examine the degree of improvement in a given

4. Impact of education on population variables (age on

education, lower secondary education or when they

indicator when individuals have completed primary

girls,

have completed upper secondary and tertiary

giving birth to first child, use of a method of contraception,

spacing births and number of children),

education;

We have referred more particularly to the areas defined in the Millennium Development Goals. 28

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 53

4. The Social and Economic Relevance of Education in Rural Areas

the second point is the differentiation between

We shall examine the results obtained in the different

rural and urban environments. This study is mainly

dimensions in turn before proposing a synthesis.

based on the rural dimension and draws on

comparisons with urban areas. The analysis of social

4.1.1 Impacts on the Risk of Poverty

is a useful vector in the perspective of transforming

The risk for a household to be in a situation of poverty

impacts starts implicitly from the idea that education

ways of life and thinking. The aim is to reduce

(interpreted here as belonging to society’s 40% poorest

handicap for achieving defined social objectives and

than in an urban environment.32 In each of the two

group)31 is globally very much greater in a rural environment

traditional conceptions and practices that are a

environments, the risk of poverty depends on the level of

to promote the adoption of more modern conceptions

education of the head of the household, as illustrated by

and practices. The hypothesis whereby the urban

environment is a factor of modernity compared to the

Graph 16 below. But it is above all in rural areas, which are

cannot be verified without

the most manifest. Thus, in rural areas, individuals with no

the most affected by poverty, that the impact of education is

rural environment, where traditions are stronger and more difficult to change,

a rigorous identification of the respective impacts of

education run a 66% risk of poverty. The risk falls to 44% if the

education in these two living environments. This

head of the household has benefited from primary education,

identification would make it possible to examine to

29% with lower secondary education, 18% with upper

what extent education can actually make an impact in

secondary education and 12% with tertiary education.

the rural context, a priori less permeable to

One can thus conclude that, globally, education, such as it

modernity, and to compare this impact with that

registered in the urban context.

is generally organized in Sub-Saharan African countries

(even if the specific relevance and quality of education

should no doubt be improved upon), has a major impact

Considering these two points, Table 14 sets out the

on the risks for individuals to find themselves in a

aggregate results for the 21 countries30 for which household

situation of poverty in adulthood in rural areas.

survey data has been mobilized.

% risk of being in 40% poorest

Graph 16. Risk of being among the 40% poorest adults depending on level of education

Rural

Urban

None

Primary

Sec1

Sec2

Level of education

Tertiary

Source: compiled by the authors based on household surveys listed in Appendix 1. 29 The urban environment does indeed facilitate modernity, through the circulation of information and the intermingling of its inhabitants, but also through the generally much more abundant availability of the instruments of modernity (medical services, bookshops, etc. within easy reach).

31 The first two quintiles have been grouped together as there is little difference in the level of wealth between the first and second quintile.

30 These countries are Benin, Burkina Faso, Cameroon, Chad, Côte d’Ivoire, Ethiopia, Ghana,

Guinea, Kenya, Lesotho, Malawi, Mali, Mozambique, Niger, Nigeria, Republic of the Congo, Rwanda, Senegal, Uganda, Zambia and Zimbabwe

Urban poverty, more visible, also exists.

55 16.7

66.1

Poverty (%)

Source: compiled by the authors based on household surveys listed in Appendix 1.

166.8

36.3

12.8

Mortality before the age of 5 (‰)

Complete vaccination (%)

Vitamin A intake (%)

Child health and infant mortality

14.2

Vitamin A intake after childbirth (%)

Postnatal check-up (%)

25.2

57.6

65.6

Medically-assisted childbirth (%)

Anti-tetanus vaccination (%)

Prenatal check-up (%)

Maternal health

Number of children

3.57

2.68

Birth spacing (years)

Use of contraception (%)

17.9

10.4

0.884

69.6

63.5

None 3.0

Age on giving birth to first child (years)

Population

Boy/girl parity

Boys (%)

Girls (%)

Access to school

Literacy (%)

Social area

Table 14. Social impact of education in rural and urban environments

43.8

117.9

48.5

22.6

25.8

23.7

49.2

75.0

84.0

3.11

2.91

18.6

20.2

28.8

85.4

53.3

27.6

31.1

27.2

64.7

78.7

89.0

2.47

3.21

19.7

27.1

0.972

95.1

92.8

17.5

59.7

56.7 31.6

36.4

31.5

76.0

80.8

91.4

1.77

3.52

20.9

32.9

0.974

95.6

93.5

Rural environments Secondary 1 Secondary 2 98.3 99.5

0.965

92.6

89.9

Primary 64.9

11.5

48.5

58.9 34.4

40.9

35.2

81.8

82.0

92.3

1.16

3.80

22.1

36.8

0.974

95.8

93.7

Tertiary 99.6

13.9

141.1

43.2 23.0

28.3

24.7

61.1

70.1

80.7

2.82

2.77

17.8

16.4

0.948

86.4

81.7

None 10.8

4.3

94.4

52.8 30.6

35.1

27.5

79.4

80.0

90.4

2.30

3.01

18.6

27.0

0.993

97.9

97.2

1.8

70.1

56.4 34.4

37.4

28.1

86.3

81.1

92.5

1.79

3.16

19.8 31.8

0.997

99.4

99.1

0.8

52.9

58.3 37.4

38.3

28.9

90.1

81.1

93.2

1.27

3.28

21.0 34.7

0.998

99.8

99.6

Urban environments Primary Secondary 1 Secondary 2 70.9 98.8 99.6

0.4

42.4

58.9 39.4

38.7

29.6

91.8

81.1

93.2

0.81

3.37

22.3 36.1

0.999

99.9

99.8

Tertiary 99.7

4. The Social and Economic Relevance of Education in Rural Areas

In an urban environment, the risks of poverty are much less

On average for all countries in our sample, the proportion of

14%, which is below that of the head of a household with

increases sharply with the length of primary schooling; but

intense overall. Individuals with no education are at a risk of

adults aged between 22 and 44 who can read easily

upper secondary education in a rural environment. With

on account of the average quality of educational services

primary education, and beyond, the risk of poverty is

(15 or 20 years ago), around 9 years of study are in fact

relatively low in urban areas (below 5%).

needed to ensure universal sustainable literacy in the adult population. Once again, urban areas have a (relatively

4.1.2 Impact in Terms of Literacy

limited) advantage over rural remembering how to read.

Reading, writing and counting skills are known to constitute

areas in terms of

a minimum reference for individuals in adulthood. To a large

The relation represented in Graph 17 corresponds to the

the educational services generally has a major effect upon

in fact noteworthy differences from country to country. Thus,

extent, these skills are acquired in school and the quality of

average situation of the different countries, while there are

what adults remember 10 or 20 years after leaving school.

for an average value of 65% literacy retention after six

The table shows that after six years of schooling, 65% of 22

years of study for rural adults, figures of around 45% are to

to 44 year olds in rural areas can read without difficulty; the

be found in Mali and Niger, 55% in Chad, 85% in Ethiopia

figure reaches 70 % in urban areas. It is probable that the

and 95% in Rwanda. These differences are mainly linked to

difference is to do with the fact that individuals in urban

the quality of education in the different countries.

areas have more opportunity to see written material than

those living in rural areas where there are fewer

4.1.3 Access

School:

However, it is also necessary to consider the length of initial

The chances of children attending school certainly depend

obtained.

reasonably close to the family home), but they also depend

Intergenerational Effects

opportunities to keep up these skills.

studies in more detail. Graph 17 shows the relationship

Impacts

and

upon where the school is located (it is preferable for it to be

Graph 17. Likelihood of being literate in adulthood (22-44 years old) depending on the duration of initial studies

% of literacy

Rural

Urban

Number of years of study

Source: compiled by the authors based on household surveys listed in Appendix 1.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 5

4. The Social and Economic Relevance of Education in Rural Areas

% access to school

Graph 18. Likelihood of being literate in adulthood (22-44 years old) depending on the duration of initial studies

Rural boys

Urban boys Rural girls

Urban girls None

Primary

Sec1

Sec2

Tertiary

Mother’s level of education

Source : compiled by the authors based on household surveys listed in Appendix 1.

upon parents’ decisions in terms of the inherent benefits

attended school bears her first child a little before the age

not correspond to the ancestral social tradition, the chances

secondary education, this will be three years later on

and costs of schooling. Insofar as the “modern” school does

of 18, whereas if she has completed both cycles of

of children going to school are generally less favorable in

average (at the age of 21).

rural areas than in urban areas, and if the mother has

herself attended school, this constitutes a contributory

There is little difference between urban and rural women in

Traditions that favor boys, and the often more intense use

environments, there is a significant tendency for more

being discriminated against compared to boys in access to

successive pregnancies. Thus, while women who have not

when their mothers have been educated. Graph 18

pregnancies on average, those who have completed

factor for children, particularly girls, to do so as well.

terms of birth spacing; but in each of these two

of girl labor in the family economy, can lead to young girls

educated women to leave a longer interval between their

school. Girls’ chances of attending school rise considerably

attended school leave 2 years and 9 months between two

presents the results obtained.

primary education leave 3 years, and this figure rises to 3 years and 5 months for a complete secondary education.

4.1.4 Impacts in Urban and Rural Areas

There are significant differences between urban and rural

Four indicators have been taken into account: i) age on first

areas in the use of a method of contraception; this may in

pregnancy (the higher the age, the fewer the children); ii)

part be due to more traditional behavior in rural areas, but

use of a method of contraception (aimed at reducing the

also to the fact that there have been fewer information

number of children); iii) birth spacing (leading to better

campaigns in these areas and that family planning services

maternal and child health and to a lower number of

are scarcer. That said, the difference is not considerable,

children) iv) the fourth indicator is the number of children a

especially when considering women with the same level of

woman has by the average age of 29.

education, as illustrated by Graph 19. In the countries in our

sample, the average rate of use of a method of

Longer studies mean women will have their first child later,

contraception by women who have completed primary

with no significant difference in this respect between urban

education is estimated at 20% in rural areas and at 27% in

and rural areas. On average, a woman who has never

urban areas (but it is possible that urban women do not use

4. The Social and Economic Relevance of Education in Rural Areas

% of use of a method of contraception

Graph 19. Use of a method of contraception depending on mother’s level of education

Rural

Urban

None

Primary

Sec1

Sec2

Level of education

Tertiary

Source: compiled by the authors based on household surveys listed in Appendix 1.

the same methods, since they may possibly have access to

secondary, as well as tertiary education, engender a

more modern and more effective methods).

specific marginal impact of around 0.5 children each. This

figure is very substantial and demonstrates a major effect of

In both environments, the women’s level of education

education on this dimension.

of contraception.33 The marginal impact appears to be

Rural areas seem a little more traditional than urban areas

primary education and those who have not attended school,

For example, women who have completed primary

exerts significant impact on their chances of using a method

particularly high between women who have followed

when comparing women with the same level of education.

while the positive impact also grows with secondary

education have around 0.8 children more in rural areas than

education but at a decreasing rate. When we consider

in urban areas.35

higher levels of education, the difference between urban

and rural areas further diminishes. We also observe that

4.1.5 Impacts on Maternal Health Factors

(both rural and urban) use no method of birth control.

Five indicators are considered in this dimension: i) prenatal

Concerning the number of children, the indicator is not the

skilled attendance at childbirth; iv) postnatal check-up; v)

even after tertiary education, around two-thirds of women

check-up; ii) antitetanus vaccination before childbirth; iii) vitamin A intake after giving birth.

final line of descent (not immediately accessible in

household surveys on account of the number of women

surveyed), but that of the average number of children for

women of around 29 years of age on average.34 Graph 20 illustrates the results obtained, and the corresponding figures are to be found in Table 14.

33 Even if the method used is not modern or very effective, it does clearly show that there is an awareness of this issue.

A sharp downward trend is clearly identified in the number

of children in connection with the woman’s level of

This indicator is therefore normally significantly lower than that of final descent.

The overall average differences in the number of children in rural and urban areas are greater due to the fact that urban women are more educated on average than those in rural areas.

education, both in urban and rural areas. The relationship is

more or less linear, and primary, lower and upper

4. The Social and Economic Relevance of Education in Rural Areas

Number of children

Graph 20. Average number of children for a 29 year-old woman depending on her level of education

Rural

Urban

None

Primary

Sec1

Sec2

Mother’s level of education

Tertiary

Source: compiled by the authors based on household surveys listed in Appendix 1.

Prenatal check-ups are relatively frequent in the

frequent for women not to have medical follow-up

through pregnancy without a prenatal visit to a

21 illustrates the relation between the probability of a

during pregnancy in the country than in towns. 36 Graph

countries studied; only a minority of women go

medical check-up during pregnancy and the mother’s

specialist or supervision by specialized staff. This is

level of education.

the case for urban and rural areas, but it is more

% prenatal check-ups

Graph 21. Prenatal check-ups depending on mother’s level of education (as %)

Rural

Urban

None

Primary

Sec1

Sec2

Level of education

Tertiary

Source: compiled by the authors based on household surveys listed in Appendix 1.

Probably for reasons of demand or of interest, in view of traditional practices, as well as supply and access to specialized services which are sometimes very far from the family home.

4. The Social and Economic Relevance of Education in Rural Areas

The pattern is identical in both types of environment.

Skilled attendance at childbirth constitutes an important

lesser tendency to have a check-up during pregnancy (66

effective if the woman requests it and if it is available locally.

proportion of check-ups during pregnancy increases very

indeed suggest the articulation of the supply and demand

education (by 10 points in urban areas and 18 points in

proportion of attendance at childbirth and the woman’s level

Women who have not attended school have a much

element of maternal health. This assistance can only be

and 81% respectively in rural and urban areas). The

The results obtained upon analysis of this dimension

substantially when women have completed primary

aspects. Graph 22 shows the relationship between the

rural areas). There is less difference when considering

of education.

higher levels of education; it is only in rural areas that

lower secondary education makes a significant additional

In the first place, the positive impact of women’s education

all, the impact of education on this aspect of maternal

Education does contribute to modifying women’s attitudes

change, although this is relatively modest (5 points). In

resulting in a demand for these services is clearly identified.

health is considerably higher in rural areas than in towns,

in what is a very important act for them. Part of this impact

bearing in mind that it is clearly primary education that

is obtained with a full cycle of primary education (+24 points

plays the central role.

in rural areas, +18 points in urban areas), but significant additional impacts, although of decreasing intensity, are

Concerning antitetanus vaccinations before giving birth, the

registered with each of the two secondary education cycles;

structure is very similar to that described for prenatal check-

with little marginal impact found for tertiary education.

ups (no doubt because women are given the antitetanus

However, large differences are also identified between

vaccination during these check-ups). However, almost 20%

urban and rural areas when comparing women of the same level of education;37 thus, there is a difference of 36 points

of well-educated women in both urban and rural areas have

for women who have never attended school and of 30

not received this vaccination.

% of assisted deliveries

Graph 22. Skilled attendance at childbirth depending on mother’s level of education (as %)

Rural

Urban

None

Primary

Sec1

Sec2

Level of education

Tertiary

Source: compiled by the authors based on household surveys listed in Appendix 1.

They are once again globally more acute between the two types of environment, since women are less educated in rural areas.

4. The Social and Economic Relevance of Education in Rural Areas

points for those who have completed primary education.

observed in the full vaccinal coverage actually carried out in

These differences no doubt reflect both the effects of the

the different countries.

services actually supplied, as well as demand, since

education alone cannot change the traditional mentalities

As a result, the impact of the mother’s education may

prevailing in the rural context.

possibly be expressed only marginally around the average

vaccinal coverage registered in each individual country.

Postnatal check-ups and vitamin A intake after childbirth are

Table 14 and Graph 23 indicate that the mother’s education

characterized by significantly lower percentages than the

has a significant impact on the chances of her children

previous three aspects of maternal health. Postnatal care is

receiving full vaccination at the age of 2 (with, however, a

has an impact, particularly primary education, in influencing

indicate a significant difference in the performance of the

rural areas. Generally speaking, the urban-rural differences

vaccination coverage is around seven points lower in the

clear structure of decreasing marginal impact).38 They also

not as popular as prenatal care. The mother’s education

systems in place in urban and rural areas. Overall,

women’s behavior as to better postnatal care, especially in

rural areas than in towns.

are not very considerable, with the exception of the case of

women who have not attended school; they tend to

Finally, the indicator of the risk of child mortality before the

perpetuate traditional behavior in rural areas, while they

age of 5 is obviously of particular importance.

seem to be “carried along” by the more favorable context and better health service provision in urban areas.

It can be observed that, in both rural and urban areas (even

4.1.6 Impact in terms of Child Health

slightly more so in rural areas), the mother’s level of education has a powerful effect on reducing the risk of child

mortality before the age of 5. Figures do in fact fall from

Three indicators are taken into account here for this

around 150 for 1 000 when the mother has never attended

dimension: i) vitamin A intake, ii) complete vaccination at

school to around 100 for 1 000 when she has completed

the age of two and iii) risk of mortality before the age of 5.

primary education, to around 80 for 1 000 for complete lower secondary education, 65 for 1 000 for complete

On average for the countries analyzed as a whole, hardly

secondary education (lower and upper) and 50 for 1 000

one child out of two benefits from vitamin A intake in early

when the mother has attended tertiary education. The

childhood; the value is around 43% in rural areas and 53%

difference is relatively modest between urban and rural

in urban areas. The impact relating to the mother’s

areas.

education is obtained with primary education completion.

12 and 10 points are gained respectively in rural and urban

areas when the mother has completed a full course of

4.1.7 Towards a Synthesis

primary education, the marginal effects are of lower

The first point to be highlighted is the existence of

primary education compared to no schooling at all. Beyond intensity.

considerable differences between countries on most of

Concerning the proportion of full vaccination before the age

have merely given the average structures.

the aspects that have been analyzed and for which we

This concerns first of all average values. For example, at

of 2, the overall average coverage for the different sample

the time of the surveys, vaccination coverage in Côte

countries is under 30%, which can be considered as

d’Ivoire, Rwanda and Zimbabwe was over 50% whereas it

insufficient. It is likely that mothers do not benefit from the

was under 20% in Mali, Uganda and Zambia.

same level of information or have the same motivation to ensure that their children are vaccinated, but it is equally

38 The specific impact of primary education stands at ten points in rural areas and eight points in urban areas, whereas the additional effect associated with the lower secondary cycle only reaches five and four points respectively.

probable that supply is patchy; a strong indicator supporting the relevance of this statement is the pronounced variability

4. The Social and Economic Relevance of Education in Rural Areas

% vaccination

Graph 23. Complete vaccination before the age of two depending on the mother’s level of education (as %)

Rural

Urban

None

Primary

Sec1

Sec2

Level of education

Tertiary

Source: compiled by the authors based on household surveys listed in Appendix 1.

Risk of mortality (%)

Graph 24. Risk of mortality for children before the age of 5 depending on the mother’s level of education

Rural

Urban

None

Primary

Sec1

Sec2

Level of education

Tertiary

Source: compiled by the authors based on household surveys listed in Appendix 1.

In terms of the measure of the impact of education, some

of “modern” social behavior. In all the dimensions studied, the

countries such as Benin, Kenya and Mozambique have an

impact of education has proved to be both significant and

education system that has a strong influence on social

powerful. That said, in this framework, it is important to

behavior, while in other countries, such as Senegal and

measure empirically what are the respective impacts of the

Zimbabwe, the education system has a lesser influence on

different levels of studies in the different social dimensions

behavior.

analyzed. In this respect, Table 15 offers an estimation of the specific impacts (differential/marginal) of the different levels of

The second point that it is appropriate to recall is that

education for each of the indicators taken separately, and

education proves to be a powerful instrument for the adoption

consolidated for each of the six dimensions studied.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010

63 40.8

Poverty

Overall score (6 dimensions)

Source: compiled by the authors using the previous results.

% impact for expenditure of 1 GDP per capita

Unit cost of the cycle (GDP/capita) 64.4

0.81

52.4

45.3

41.3

Full vaccination

Mortality before the age of 5

45.3

54.0

Child health

Vitamin A intake

37.8

45.0

42.5

71.0

Vitamin A intake after childbirth

Postnatal check-up

Medically-assisted childbirth

Antitetanus vaccination

68.8

53.0

Maternal health

Prenatal check-up

18.9

Number of children

37.2

21.2

9.5

18.8

1.27

23.8

27.5

23.5

24.9

21.3

21.8

17.0

27.3

15.2

18.9

20.1

26.6

26.3

26.1

26.0

87.9

9.8

9.7

Birth spacing

Use of contraception

34.6

87.7

87.4

64.1

1.2

8.1

1.76

14.3

20.7

21.7

18.3

19.0

15.2

21.7

20.4

20.0

8.7

8.8

15.9

29.3

27.8

21.7

28.8

2.0

2.1

Rural environments Primary Sec 1 Sec 2

17.3

None

Age on giving birth to first child

Boys

Girls

Access to school

Literacy

Social area

Table 15. Specific impact of the different levels of study on social dimensions

9.4

0.8

11.13

11.0

9.5

13.0

9.6

10.8

18.7

17.5

10.2

5.1

3.5

11.0

25.1

24.7

15.0

27.8

0.6

0.7

0.1

Tertiary

None

102.1

0.63

63.9

71.5

39.5

46.0

54.5 61.2

66.0

56.4

59.7

90.1

77.6

69.9

25.9

39.9

53.6

17.9

85.5

85.7

85.9

67.6

21.7

0.95

20.7

18.6

20.5

23.6

21.4 22.8

22.1

12.9

22.5

9.9

16.9

25.3

25.1

24.4

26.5

11.0

10.6

10.3

31.4

5.2

1.76

9.1

7.3

14.6

18.4

13.2 12.4

8.5

16.2

12.4

0.0

5.5

8.5

25.8

19.6

14.8

28.7

2.7

0.9

Urban environments Primary Sec 1 Sec 2

5.5

0.5

11.13

2.6

8.8

12.0

6.8 3.6

3.5

14.5

5.5

0.0

4.7

23.0

15.5

7.3

27.0

0.9

1.0

0.1

Tertiary

4. The Social and Economic Relevance of Education in Rural Areas

Graph 25. Specific impacts of different education levels on overall social dimension

Rural

Impacts (%)

Urban

Cumulative rural

Cumulative urban

None Source: compiled by the authors using Table 15.

Primary

Sec1

Sec2

Level of education

If we first look at the six social dimensions studied as a

Tertiary

expenditure. The indicator for primary education is seen

whole (last 3 lines in Table 15 and Graph 25), we clearly

to be around three times greater than for lower secondary

see that primary education has the highest impact with 52%

education.

impact of lower secondary education is much lower than

This overall structure does however differ somewhat in the

urban areas). Upper secondary has an even lower

primary education is especially important for literacy,

areas). The specific contribution of tertiary education is

education, and more particularly lower secondary, does add

in rural areas and 64% in urban areas. The additional that of primary education (24% in rural areas and 21% in

six social dimensions studied. In rural areas, the impact of

additional impact (14% in rural areas and 9% in urban

access to education and maternal health. Secondary

extremely low.

something to the contribution of primary schooling, but the

The predominance of primary education in producing

for child health, while primary education maintains the

strengthens the impact of primary education) is confirmed

substantial additional contribution. As far as the dimension

latter plays the leading role. As for the risk of poverty and

social impacts (while lower secondary education

highest impact, secondary education does make a very

when looking at the specific impacts of the different levels

of population is concerned, continuity is to a great extent

of education and the public expenditure involved in

the prevailing factor in both geographic environments, with

implementing them (penultimate line in the table). We can

each level of education playing a role in significantly

then calculate an indicator measuring the relative impact

improving the different selected indicators, through to

obtained for one unit of GDP per capita of unit

tertiary education.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 64

4. The Social and Economic Relevance of Education in Rural Areas

4.2

Rural Areas and the Economic Dimension: the Economic Impacts of Education

Three aspects are addressed here: i) the contextual

the working population, its distribution across the different

the past twenty years and its distribution across the

different sectors. Table 16 provides the initial quantitative

apparent labor productivity in a temporal perspective and its

sector of activity.

intersectoral mobility; iii) the analysis of the economic return

The figures indicate that on the overall sample the working

analysis of the working population, how it has evolved over

sectors of activity and average labor productivity in these

different sectors of economic activity; ii) the estimation of

information on the working population and its distribution by

consequences in terms of the working populationâ&#x20AC;&#x2122;s on educational investments in the rural world.

population has increased from 131 million individuals in

1985 to 211 million individuals in 2003. This increase

4.2.1 A Macro and Temporal Perspective of the

corresponds to a multiplication by 1.61, demonstrating an

Working Population

average growth rate of 2.65% per annum between the two

dates. However, during this period, developments as to the

The economic impacts of education in a country are

number of people per economic sector of activity are very

necessarily part of the overall national context concerning

contrasted.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 65

180.2

3 193

2 298

2 476

551

681

2 837

Mauritania

Niger

Rwanda

Rep. Congo

Nigeria

Malawi

2 463

762

27 804

3 100

3 177

4 372

Mali

Madagascar

7 615

Lesotho

Kenya

351

301

Guinea Bissau

Gambia

5 494

Guinea

Ghana

19 735

1 142

184

3 516

141

1 974

1 152

3 539

3 775

1 448

43 817

5 180

5 515

1 060

4 903

7 534

647

13 987

569

583

4 072

8 880

28 894

1 506

287

6 285

228

3 250

1 727

148

5 843

153.3

190.1

157.6

182.6

177.9

155.6

154.4

172.3

117.4

183.7

162.0

193.5

164.5

161.6

146.4

131.9

156.3

178.8

162.0

164.7

150.0

154.6

165.1

138.9

160.2

163.0

2 878

5 061

6 247

1 597

3 159

3 832

Index 2003

Overall Numbers (000) 1985 2003

Ethiopia

Eritrea

Djibouti

Côte d’Ivoire

Comoros

Chad

CAR

Cape Verde

Cameroon

Burundi

Burkina

Benin

Angola

Sector Indicator Years

2 268

303

17 641

2 355

2 653

410

2 559

3 270

406

5 980

255

188

1 650

3 360

17 100

900

1 936

1 493

668

2 040

2 092

2 626

1 011

2 500

2 729

114

12 003

3 842

4 042

401

2 406

4 048

367

9 448

303

117

2 022

2 470

21 986

1 042

1 953

102

1 984

700

1 886

2 814

3 601

1 105

2 820

Numbers (000) 1985 2003

120.4

37.7

68.0

163.1

152.3

97.7

94.0

123.8

90.6

158.0

118.8

62.0

122.6

73.5

128.6

115.8

70.1

100.9

102.8

132.9

104.8

27.7

92.4

134.5

137.1

109.3

112.8

92.1

39.7

63.4

83.0

85.6

60.2

80.6

74.8

73.5

78.5

72.8

62.5

66.6

61.2

86.6

78.8

24.0

55.1

70.5

75.6

58.0

57.6

91.0

83.1

63.3

65.2

72.3

7.9

27.4

74.2

73.3

37.8

49.1

53.7

56.7

67.5

53.3

20.0

49.7

27.8

76.1

69.2

10.7

31.1

44.7

61,0

40.5

10.3

32.3

88.1

71.2

38.4

45.1

Agriculture Index % Working Pop. 2003 1985 2003

714

6,5

202

8,3

2,8

130

1,5

1,6

7,1

151

103

170

694

7,2

202

8,0

3,4

131

1,7

1,6

6,8

147

101

170

Numbers (000) 1985 2003

Table 16. Evolution of the working population in a broad sample of low-income countries in Sub-Saharan Africa between 1985 and 2003

106.1

102.2

97.2

105.1

102.6

110.5

102.6

114.0

100.4

100.1

95.8

121.6

99.0

101.3

117.1

101.5

112.1

96.7

100.3

100.2

90.1

95.4

97.2

88.6

97.5

96.8

100.1

2.3

2.6

0.9

3.0

1.0

0.8

1.9

3.3

2.6

2.4

0.9

1.9

2.4

0.2

2.8

0.8

2.6

1.1

2.4

2.6

7.4

4.3

3.8

3.3

2.6

4.4

1.6

1.2

1.6

0.5

1.7

0.7

0.6

1.2

2.8

1.4

0.6

1.1

1.5

0.1

2.2

0.6

1.4

0.7

1.5

1.6

4.6

2.5

2.4

2.0

1.4

2.7

Industry Index % Working Pop. 2003 1985 2003

139

441

9 448

456

353

264

591

1 019

128

1 434

110

780

2 005

2 600

210

138

1 490

433

454

1 348

119

429

545

1 162

987

1 316

31 120

1 311

1 378

651

2 471

3 392

262

4 337

257

463

2 004

6 279

6 867

432

254

4 245

124

1 219

1 000

126

3 811

302

1 359

1 734

3 257

Numbers (000) 1985 2003

707.8

298.0

329.4

287.6

390.1

246.6

417.8

332.8

204.8

302.5

295.0

419.8

257.0

313.2

264.1

205.5

184.3

285.0

311.7

281.2

220.3

376.3

282.6

253.6

316.6

318.0

280.2

5.7

58.0

34.0

16.1

11.4

38.8

18.6

23.3

23.2

18.8

24.9

36.6

31.5

36.5

13.2

18.4

75.2

42.4

28.4

22.0

39.4

35.0

38.1

5.2

13.6

34.1

30.3

26.1

90.9

71.0

25.3

25.0

61.5

50.4

45.0

40.5

31.0

45.2

79.4

49.2

70.7

23.8

28.6

88.7

67.5

54.6

37.5

57.9

85.1

65.2

9.5

26.9

60.2

52.1

Services Index % Working Pop. 2003 1985 2003

4. The Social and Economic Relevance of Education in Rural Areas

3 103

131 360 211 438

5 461

4 579

17 70

2 172

315

9 462

4 181

161.0

176.0

168.0

174.2

181.8

163.8

139.4

133.7

165.3

2 391

2 040

11 132

833

197

5,7

3 671

1 458

94 937 106 079

2 089

1 434

8 039

781

132

5 187

1 395

Source: World Bank Indicators and International Labour Organization (ILO).

Overall

2 726

Zimbabwe

Zambia

10 162

1 195

192

7 075

2 529

Tanzania

Togo

Swaziland

Sao Tomé & P.

Sudan

Senegal

111.7

114.5

142.3

138.5

106.6

149.2

32.4

70.8

104.5

72.3

67.3

52.6

79.1

65.4

68.7

56.4

73.3

55.2

50.2

43.8

44.6

62.9

38.4

62.5

13.1

38.8

34.9

2 960

139

230

3,2

0,5

134

2 997

146

236

3,5

0,5

157

101.2

105.3

105.9

102.5

96.9

108.5

100.6

116.8

114.9

2.3

4.5

3.3

2.3

4.6

1.7

1.5

1.9

3.3

1.4

2.7

2.1

1.3

2.4

1.1

1.7

2.3

2 924

2 444

6 338

1 286

114

5 634

2 628

33 462 102 362

875

1 202

1 892

359

1 754

1 052

305.9

334.1

203.3

334.9

358.6

200.8

284.1

321.3

249.9

25.5

28.2

44.1

18.6

30.0

29.7

42.1

24.8 41.6

48.4

53.5

53.4

35.8

59.2

36.3

85.8

59.5 62.9

4. The Social and Economic Relevance of Education in Rural Areas

The agricultural sector employed the largest number of

These can be considered as average developments for the

that, while numbers are certainly on the increase (from 94.9

but at different degrees, for most of the countries

relatively modest evolution (+11 million out of the increase

working population over the period, the figures are

+17% in relative terms compared to the 1985 figure, while

observed in countries of a comparable level of development

period), which means that the share of agricultural

around 3.5% per annum in countries such as Kenya,

workers both in 1985 and in 2003. But it can also be seen

sample of countries analyzed; they are also valid globally,

million individuals in 1985 to 106.1 million in 2003), this is a

considered. Concerning the progression in volume of the

of 80 million in the overall working population, or rather

generally high (particularly if we compare them with those

the working population has increased by 61% over this

in Asia or in Latin America) with average growth rates of

employment in total employment falls from 72.3% in 1985

Gambia, Malawi, Republic of the Congo and Togo. But the

1.2 percentage points.

Burundi, Lesotho and Sudan, with growth rates in the

The industrial sector corresponds on average to a very

over the period studied.

to only 50.2% in 2003, i.e. an average annual decrease of

progression was much less dynamic in countries like

working population of around, or under, 1.5% per annum

small proportion of the working population in the countries

considered. In 1985, this sector only employed 2.3% of the

The structure of the working population in the different

aggregate terms on the sample countries as a whole, there

example, the share of agricultural employment in

overall working population. Between 1985 and 2003, and in

countries shows significant differences. In 2003, for

was practically no development in the numbers employed in

the working population (with an average value for this

this sector, the number observed in 2003 (a little under 3

sector of 50.2%) can vary from figures of around 30%

million) being virtually identical to that observed in 1985. As

(or less) in countries such as Cameroon, Côte

a result – and in view of the substantial increase in the

d’Ivoire, Gambia, Ghana, Nigeria and Republic of the

employment falls and only accounts for 1.4% of

Burkina Faso, Burundi, Ethiopia, Malawi, Niger and

working population over this period – the share of industrial

Congo, to figures of over 70% in countries such as

employment over the recent period.

Rwanda. But there is a downward trend in the

proportion of agricultural employment (22 points down

The most dynamic development concerns the working

during the 18 years from 1985 to 2003, i.e. 1.2 points

population employed in the service sector. Indeed, while

per annum on average) in all countries; this has

1985, which represented 25.5% of the total working

countries listed above (9 points down on average over

there were 33.5 million individuals in this sector of activity in

certainly been less pronounced in the second group of

population at the time, there were 102.4 million in 2003; the

the period) than in the first (32 points down on

service sector thus accounted for 48.4% of total

average over the period).

employment at that date (i.e. very similar to agricultural

The share of industrial employment is very limited in all

employment). This is all the more remarkable in that, on an overall expansion of the working population of 80 million

the countries making up the sample. Thus, in 2003, there

sector (86% of “new jobs”). While remarkable from the

industrial employment was over 2.5% (Angola, Cameroon,

over the period, 69 million are accounted for by the service

were only a few countries where the estimated proportion of

quantitative angle, this development is no doubt also a little

Cape Verde, Lesotho, Zimbabwe) whereas this proportion

expansion. It can indeed be asked to what extent this sector

the sample, this statistic dropped between the years 1985

growing general difficulties on the labor markets of the

the industrial sector in 1985 in eleven countries, the figure

worrying in terms of the productive dimension of this

was only around 1% for many countries. In all countries in

ensures a production or redistribution function concealing

and 2003; while over 3% of the working population was in

countries studied; we shall come back to this aspect later.

had dropped to 1% in 2003.

4. The Social and Economic Relevance of Education in Rural Areas

The relative weakness of industrial employment implies that

connection between the International Labour Organization-

there is a very strong negative relation between the

type macroeconomic statistics presented above and those

proportion of agricultural employment and that of

resulting from household surveys. Some people may thus

employment in the service sector, those countries with a

be characterized as not working or part of the working

population working in the service sector, and vice versa.

the mixed bag of services in the macro grouping. In

high agricultural population having a low proportion of the

population but unemployed in a household survey, and in

addition, this category corresponds to an infinite variety of

It is interesting to go a little further than the global statistics

jobs, since it includes just as well a tailor craftsman

distinction between modern employment and informal

reasonable income as a young woman hairdressing in the

and introduce two complementary dimensions, namely the

employing several people and enjoying a regular and

employment on the one hand, and urban and rural

street with a customer every two days, or her brother selling

environments on the other. In the grouping per sector, the

individual cigarettes on a half-time basis.39

case of the agricultural sector is clear-cut; it is primarily

rural and informal; there are of course urban market

Geographically, this intermediate category has a strong

remain marginal.

individuals concerned live in urban areas, where they

urban dimension since, on average, almost 50% of

gardeners and some formal farming concerns, but they

represent 68% of total employment. On the other hand,

around 50% of the working population in this category

Concerning modern (public and private) employment, this is

currently live in rural areas, where the sector is estimated at

also a priori fairly easy to define, given that this employment

representing around 28% of total employment.

sector i) is relatively limited in terms of numbers and of

proportion of total employment (estimated at around 10% in

Although covering a wide range of activities and in spite of

the recent period with overall variations of between 4 and

15%, depending in particular on the level of development in

its uncertain statistical contours, this non-agricultural

proportions over time (thus, the proportion of modern jobs

quantitative estimations are indeed imprecise, this category

informal sector is of particular interest in that, although the

the different countries) and ii) has only developed in modest in total employment has remained basically stable at

is undergoing considerable development as we can see

distribution of modern employment is focused on urban

agricultural employment mentioned above. Table 17

27% of total employment; 40% of modern employment is to

of the working population.

with the dynamics of modern employment and of

around 10% between 1990 and 2004). The geographic

proposes reasonable orders of magnitude in the evolution

areas (60% of modern employment is urban, representing

be found in rural areas where it only represents a tiny proportion [more or less 5%] of total employment).

According to these estimations, and partly using the result

With little or no increase in the proportion of modern

in many countries in the region, the evolution of the service

of the analysis of data from household surveys carried out sector identified above essentially reflects that of non-

employment, and only a small increase in numbers and a

agricultural informal employment. It is indeed estimated that

significant fall in the proportion of agricultural employment,

non-agricultural informal jobs increased from 23 to 83.6

the category named non-agricultural informal employment

million between 1985 and 2003. Over this period, the

generously takes in all those not belonging to the previous

number of jobs increased by 80 million (211.4-131.4) with

two categories.

This is necessarily a relatively indistinct category, due in

part to the reality of the economic activities concerned and also to the statistical evaluation of this category. A measure

39 In such a variety of situations where income is extremely low, it is not easy to define the “border” between employment and non-employment.

of this vagueness can be seen in the fairly imperfect

4. The Social and Economic Relevance of Education in Rural Areas

Table 17. Tentative estimation of the evolution of the working population in Sub-Saharan Africa, 1985-2003 Millions

Working population Agriculture

1985

131.4

Number

2003 Urban

211.4

61.2

150.2

13.1

8.7

41.8

94.9

106.0

Services

33.5

102.4

Informal

20.9

81.8

Modern

12.6

3.0

Informal

2.1

1.9

0.9

1.1

Modern

13.5

21.8

Industry

0.9

1.1

Services

Non-agricultural informal Services Industry

12.6

20.6

23.0

83.6

2.1

1.9

20.9

99.6

20.6

Industry Modern

6.4

Rural

81.8

Source: compiled by the authors using Table 16.

4.2.2 Measure and Evolution of Apparent Labor

11.1 million in agriculture, 7.3 million in modern

Productivity and its Consequences

employment and 60.6 million in non-agricultural informal

employment. The latter therefore accounted for 76% of new

We now have some idea of the evolution of the working

jobs over the period in the region as a whole.

population and its distribution during the 1985-2003 period

This is a very strong observation; it is also very reliable

information on the measurement of the value added of the

the proportion of non-agricultural informal employment in

individuals employed alongside the value added in the

and we are to pursue the macro perspective by mobilizing

since the imprecision of these figures no doubt means that

different economic sectors. Putting the number of

the increase in jobs could be somewhere between 70 and

different sectors enables an estimation of average apparent

A question arising from this observation is to determine if

2003 period, we can analyze the evolution of labor

80%, which does not modify the conclusion of the analysis.

labor productivity. As both data are available for the 1985-

this trend brings progress or difficulties. One way to

productivity in the different economic sectors in the different

proceed is to look into the issue of labor productivity in the

countries over that period. Table 18 indicates the results

relevant countries.

obtained.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 70

4. The Social and Economic Relevance of Education in Rural Areas

Table 18. Apparent labor productivity by sector of activity, 1985-2003 (regional average, US dollars of the year 2003) Apparent labor productivity Overall

Agriculture

1985

1990

806.1

299.8

Services

Non-agricultural informal Industry

1995

819.4

1 298.8 11 473

802.0

363.0

451.1

498.9

730.0

600.0

550.0

508.9

846.5

13 520

Source: compiled by the authors using Table 16.

2003

803.3

348.0

1 007.4

912.9

2000

770.5

728.1

13 290

For the countries in Table 18 taken as a whole, the average

15 662

685.7

15 503

It can be noted that this initial comparative observation of

value of apparent labor productivity would have remained

labor productivity in the agricultural sector and in the non-

very much unchanged between 1985 and 2003, at around

agricultural informal sector is coherent with the intersectoral

us now examine the relative situation of the agricultural sector

individuals employed in the non-agricultural informal

800 dollars, in constant monetary value (of the year 2003). Let

mobility observed and a high increase in the number of

and the service sector. In 1985, average productivity in the

sector.40

agricultural sector was particularly low (300 dollars of 2003),

while that of the service sector was considerably higher (1 300

Moving along now from 1985 towards the current period, the

(public and private) service sector where the pay level is much

agricultural informal sector differ within the overall informal

trends observed in the agricultural sector and the non-

dollars). But this is the aggregate component of i) the modern higher and ii) the informal (non-agricultural) sector for the area

sector. Data in Table 18 and Graph 26 provide a clear

the basis of the share of the modern sector and of its

more or less continuous improvement in labor productivity in

illustration in this respect. Since 1985, there has been i) a

of services where labor productivity is considerably lower. On

agriculture (whereas the number of individuals employed in

estimated average level of remuneration, we have estimated

this sector has hardly increased) and ii) a progressive fall in

the average apparent productivity in the informal service

sector at around 913 dollars (of the year 2003) for 1985. This

average apparent labor productivity in the non-agricultural

apparent labor productivity in agriculture.

has increased (sharply as we can see in Table 16).

informal sector, while the number of individuals it employs

is therefore considerably higher than the figure estimated for

Graph 26. Evolution in labor productivity by sector of activity, 1985-2003

1 400

(2003 S)

Labor productivity($2003)

1 200 1 000

Agriculture

800

Services

600

Non-agric. informal

400 200

1985

1990

Source: compiled by the authors using Table 18.

1995

Years

2000

2003

40 The modern sector is obviously very attractive due to higher income possibilities, but access

is regulated, whereas this is not the case in the informal sector.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 69

4. The Social and Economic Relevance of Education in Rural Areas

The upward trend of labor productivity in agriculture

possible to identify the professional activity and type of

agricultural informal sector leads to a convergent situation,

information on the individuals’ schooling history. The

combined with the fall in labor productivity in the non-

job filled, in addition to providing sufficiently precise

as represented in Graph 26. Data for the year 2003 shows

second perspective is more demanding in terms of

virtually equal apparent labor productivity in these two

statistical data insofar as it requires the availability of an

structure will slow down the very strong dynamics

two perspectives as far as possible.

sectors of activity. It remains to be seen to what extent this

estimation of monetary income. We have followed these

registered over the last twenty years, characterized by a

sharp increase in the number of those working in the non-

Measuring per level of wealth and living conditions

The current situation of a drop in average labor productivity

index is set by fixing the level of the factorial score at 100

questioning on the type of incentive structural actions and

of the heads of households working as farmers, breeders or

agricultural informal sector.

Table 19 gives the results obtained for the wealth index (the

in a sector in quantitative expansion naturally leads to

in each country for farmers who have not attended school)

skills training for this sector insofar as it can no longer (and

fishermen in the fourteen countries where this analysis was

this has probably been the case for many years) effectively

conducted; it can be noted that these are primarily French-

absorb individuals who are not and who will not become

speaking countries.

farmers and who will not be able to find employment in the modern sector of the African economies. 4.2.3 Economic

Returns

Investments in Rural Areas

In all countries, the wealth index significantly increases for

farmers along with the level of studies reached in their

Educational

youth, albeit to varying degrees from one country to

another. Out of the 14 countries analyzed, this index rises from the value of 100 set conventionally for those who have

We shall now shift the perspective to individual level. The

not attended school to 192 for those who have completed

question then is to determine to what extent individuals

primary education, 252 for those who have completed lower

school. The situation of the (heads of) households can be

completed upper secondary education.

identified by applying a factorial method on the basis of

On account of its construction, the index does not constitute

also be brought down to individual level by using a

differential calculations according to the level of education,

disregarding the non-monetary share that can be quite

indicator for each level (differential index) and, on the other

enjoy better living conditions when they have attended

secondary and 297 for the (very few) farmers who have

looked at through the measure of their “wealth” indicator

the household’s assets and living conditions. This can

a cardinal measure. By way of illustration, we have made

measure of monetary income (but that leads to

in order on the one hand to generate a specific intensity

substantial for some farmers). In terms of the availability

hand, to calculate a cost-effectiveness indicator by relating

countries where there is a household survey making it

costs of schooling.

of data, the first perspective can be followed in many

the wealth index differentials to an estimation of the direct

4. The Social and Economic Relevance of Education in Rural Areas

Table 19. Impact of the farmers’ level of education on the wealth index in 14 countries Country

Level of education Secondary 1

None

Primary

Benin

100

312

471

601

Burundi

100

139

166

188

Burkina Faso Cameroon Chad

Congo

Ghana

Guinea

Guinea-Bissau

Malawi

100 100 100 100 100 100

100

189 304

203

130

155

316

467

142

179

248

368

220 508 233 175 583 211

469

160

235

309

168

188

190

100

103

Rwanda

100

145

100

425

162

Mali

Mauritania

216

Secondary 2

161

165

Senegal

100

168

224

271

Overall

100

192

252

297

127.6

48.8

25.0

Differential index

Cost effectiveness index

Source: compiled by the authors using household surveys on the labor market (see Appendix 1).

On the basis of the wealth indicator, the differential index

lower intensity, which is confirmed when introducing the

between successive levels of education decreases as we

financial dimension.

and primary education, to 60 points between primary and

Measurement by the level of individual monetary

move up the levels, from 92 points between no schooling

lower secondary, and 45 points between lower and upper

income

specific impacts at each level of study are considered in the

This analysis was possible in nine countries.41 As for the

secondary. The decrease is even more significant when the

light of related production costs, with figures of 127.6 for

previous point, we examine the situation of farmers and

upper secondary. These figures should of course not be

Table 20 summarizes the results obtained.

individuals working in a non-agricultural informal activity.

primary education, 48.8 for lower secondary and 25 for

considered as orders of magnitude, particularly due to the

absence of a metric of the wealth indicator at the basis of the analysis.

However, the differences are such that it can be assumed probable a priori i) that education does have a substantial

impact on the standard of living for farmers and ii) that this impact is particularly high with primary education; the

These are Burkina Faso, Cameroon, Chad, Côte d’Ivoire, Madagascar, Mali, Mauritania, Sierra Leone and Uganda.

impact is again substantial with secondary education but of

337

286

Burkina Faso

Chad

411

286

341

Mali

411

55.9 %

24 .1 %

18.8 %

16.6 %

42.7 %

53.1 %

9.9 %

29.5 %

14.6 %

22.3 %

9.1 %

% non-agricultural rural informal jobs

60.0 %

62.6 %

69.8 %

32.1 %

32.4 %

71.2 %

58.4 %

74.7 %

82.9 %

% farmers in working rural population

32 126

88 611

21 645

6 426

76 721

17 464

19 361

12 315

24 116

48 321

79 817

24 814

14 864

156 057

29 629

21 250

13 430

32 873

46 546

24 180

16 867

16 941

128 791

10 863

15 471

7 733

5 691

Primary

76 674

75 386

32 134

12 753

222 221

40 250

29 164

20 212

61 368

125 320

25 917

27 986

36 526

207 546

13 365

27 707

9 203

6 419

Sec 1

144 505

70 970

42 557

7 471

255 595

49 438

42 267

33 022

122 043

248 292

28 024

41 884

82 656

349 834

16 957

52 928

11 499

7 448

Sec 2

Income (national currency / month) None Primary Sec 1 Sec 2

27 573

21 609

9 418

10 636

100 854

10 080

11 772

7 748

5 611

None

Income (national currency / month)

Source: compiled by the authors using household surveys on the labor market (see Appendix 1).

234

Average

Uganda

193

409

Sierra Leone

Mauritania

311

793

Madagascar

Côte d’Ivoire

793

Chad

Cameroon

337

Burkina Faso

Non-agricultural GDP/cap rural informal (USD 2003) Education (complete cycles)

234

Average

Uganda

193

409

341

311

793

Sierra Leone

Mauritania

Mali

Madagascar

Côte d’Ivoire

Cameroon

Education (complete cycles)

GDP/cap (USD 2003)

Agriculture

1.46

1.50

0.90

1.15

2.31

2.03

1.70

1.10

1.09

1.36

1.96

2.39

0.85

1.48

1.98

2.90

2.30

1.51

1.64

2.54

1.61

2.69

1.07

1.66

2.16

1.61

1.23

1.79

1.19

1.13

2.72

4.50

0.80

1.97

1.16

3.33

2.83

2.18

2.68

5.06

1.57

1.98

1.08

1.50

2.26

1.69

1.27

1.91

1.25

1.16

6.2%

6.3%

-1.6%

2.7%

15.4%

16.4%

8.4%

1.5%

1.2%

5.2%

5.2 %

9.8%

2.0%

13.2%

9.9%

5.5%

1.3%

5.2%

0.0%

0.2%

3.2%

5.3%

7.0%

16.0%

-1.7%

6.2%

-3.3%

7.7%

5.8%

7.5%

7.2%

17.3%

16.0 %

42.3%

1.8%

22.0%

28.9%

15.3%

5.8%

19.8%

4.8%

9.3%

21.8%

-1.5%

9.0%

-4.3%

3.1%

4.4%

12.1%

13.4%

25.5%

20.7 %

49.1%

2.7%

16.6%

42.1%

22.9%

9.0%

30.3%

8.3%

Rate of private value added of education Primary Sec 1 Sec 2

Ratio to previous cycle of education Rate of private value added of education Primary Sec 1 Sec 2 Primary Sec 1 Sec 2

1.32

1.69

1.12

1.79

1.59

1.28

1.08

1.31

1.00

1.01

Ratio to previous cycle of education Primary Sec 1 Sec 2

Table 20. Income and returns to education for farmers and for those in the working population employed in the non-agricultural informal sector in rural areas

4. The Social and Economic Relevance of Education in Rural Areas

Let us focus firstly on the income of individuals working in

The observation of this diversity from country to country in

making up the sample, the higher the level of education,

raise questions. Indeed, if the country figures are accepted

agriculture. It can be observed that, in all nine countries

the value added of studies in the agricultural sector does

the higher the farmers’ average income. Thus, as an

as realistic, it would be important to understand the reasons

average value for the nine countries, farmers who have

and the contextual or political factors liable to account for

completed primary education earn 32% more than farmers

these differences. We do not have the relevant information

lower secondary education earn 62% more on average

analysis. In this perspective, the hypothesis could be tested

who have not attended school; those who have completed

at our disposal in this study to satisfactorily conduct this

than those who ended their studies upon completion of

by which farmers’ education would above all be likely to

primary education; finally, those who continued their studies

have an impact when a country moves away from

through to the end of upper secondary education (they are

subsistence farming, small surface areas and the use of the

few in number) earn 57% more on average than those who

most traditional techniques.

stopped studying at the end of lower secondary education.

There is of course no direct indicator on this aspect.

However, it is important to go beyond the measure of

However, it can be supposed that countries with a greater

the measurement of the rates of private returns, bearing in

traditional agriculture. The transcription of the hypothesis

In average terms, private return on

expected to have little impact on productivity in countries

income and move on to that of returns. We have opted for

proportion of farmers in the working population will have

mind that these have been estimated using the said

“shortcut” method.

expressed above is that the education of farmers would be

primary education in agriculture is estimated at 5.2%, which

where there is a large proportion of farmers in the working

is a positive value but can be considered relatively low.

population, but that the education of farmers could

What is interesting is that private return on general studies

potentially play a more important role in countries where the

(with no explicit vocational training for agriculture) is higher

proportion of the population employed in agriculture is

average value (16%); in upper secondary, the rate is still

testing this relationship. The econometric estimation makes

for lower secondary education, reaching a quite substantial

lower. We are in possession of the data necessary for

higher (20.7%), but this figure should be regarded with

it possible to define the variability in the rate of return of

some care due to the small number of individuals

primary education in the agricultural sector. The equation

concerned in the sample; that said, this does in any case

obtained is as follows:

general is significant in the agricultural sector.

η private to primary in agriculture = 15.4 – 0.177%

mean that the value added of secondary education in

farmers in the working population (t=2.4)

These average indications are indeed very interesting. But

one essential observation to be taken into account is that

R²=0.44

there are extremely varied situations across the nine

countries studied. For example, concerning the value added

of primary schooling, the average figure of 5.2% for the rate

of private return on studies results from figures varying from

0% (Cameroon) to 13.2% (Mauritania). Even greater differences are sometimes registered in the returns on

secondary education. Over and above the overall variety, the

This is the estimated income for individuals who have completed full cycles of study (primary, lower and upper secondary).

structure of returns across the different levels of education

with higher figures in secondary than in primary education is

43 The rate of return connected to level i is calculated as the ratio of the difference in income between the levels of study i and (i-1) and of the individual’s income at level (i-1) multiplied by the number of years of study of the cycle (i-1).

clearly identified in all countries making up the sample.

4. The Social and Economic Relevance of Education in Rural Areas

This analysis must of course be considered as tentative and

the non-agricultural working population not working in the

partial; the results show that the context has a major impact

modern sector in fact face unemployment. More

traditional contexts are not favorable to the development of

heterogeneity within the population working in the non-

on the returns on studies in the agricultural sector and that

generally, this observation reflects the pronounced

the impacts of education. In a way, this result is in line with

agricultural informal sector. Indeed, while it can be

shows that returns on education for farmers are high in

identified as “non-agricultural informal” is unemployed,

that of Rosenzweig

in his study on India in which he

estimated that around one person out of every three

States concerned by the “green revolution”, unlike States

we must also consider the wide variation in the economic

not concerned by it.45

situation of people actually employed in the non-

agricultural informal sector (measured by the low value of

Finally, let us examine the situation of individuals working

the Mincer equation R²), a sector which has extremely

in the non-agricultural informal sector in rural areas.

disparate income levels.

One initial observation is that the definition of this

population is substantially different from the one figuring

In this “game”, an individual’s education seems to play quite

here the population working in a well-identified economic

educated individuals have a higher income than those that

the non-agricultural working population living in rural

significant part of some farmers’ income is not monetary) is

in international labor classifications. Indeed, we examine

a significant role overall as, fairly systematically, more

activity that provides them with a regular income, and not

are less educated. In general, i) monetary income (but a

areas. On comparing the data based on the non-

higher for individuals working in non-agricultural informal

agricultural rural working population with that on the

activities than for those in farming, bearing in mind that ii)

geographic context, we estimate that around one third of

sector of the national economies.

population working in a well-identified activity in the same

the value added of education is also relatively high in this

44 Rosenzweig, M. “Why are there returns to Education?” American Economic Review, Papers

and proceedings Vol. 85, n°2, 1991.

This result can also be found in research by Gurgand, « Education et productivité agricole en Côte d’Ivoire », Revue d’économie du développement, n°4, 1993.

Conclusion

In this study, we have examined diverse aspects of the

illiteracy (37% of rural dwellers in general, and only 25% of

situation of education in rural areas, in the largest possible

poor rurals complete a full cycle of primary education

number of low-income Sub-Saharan African countries. The

compared to 68% of urban dwellers and 80% of rich urban

main fields analyzed were i) rural schooling (what is the

dwellers).

extent of the lag compared to urban schooling, and what

process is behind this lag in the education system); ii) the

In order to reach the goal of universal primary completion,

particularly for lower secondary education; iii) the issue of

from that background who are not completing primary

These are indeed three clearly separate aspects that are

account in the different actions that will be implemented.

organization and cost of educational services in rural areas,

rural areas must be targeted, since it is primarily children

the value added of investments in education in rural areas.

education today. The analyses conducted in this study

strongly emphasize the need to take rural specificities into

worth examining individually; however, they have links

which give an understanding of how the situation in the rural

Let us rapidly go back to some of the salient results for each

taken into account: i) the importance of having a school

These specificities concern first of all the supply of

educational services, with two important dimensions to be

world relates to the educational sphere in African countries.

that is free, of good quality and close to populations

of these aspects.

(progress has clearly been made on this aspect in most

A) Rural children are significantly behind with

countries, but more still needs to be done in many countries

schooling, since education systems tend to develop from

and in certain areas in most of them). The school must of course have the right number of qualified teachers (existing

the easier to more difficult contexts (both in reference to service provision and to schooling demand). “Rich urban”

empirical studies show that the allocation of personnel is

Rural populations, and especially those living in poverty

to be improved – assignment criteria actually applied,

often insufficient in rural areas and that this situation needs

dwellers benefit first to the detriment of poor rural dwellers.

incentives for teachers to work in difficult areas, etc.) and

(more intensely so for girls in some countries), fall further

behind at higher levels of education, but the urgent

these teachers must be actually present to deliver the

dwellers include: i) those who have no access to school

must be flexible and adaptable locally (school times, school

priority for them is clearly primary education. Poor rural

required annual 900 hours of teaching; ii) school supply

year, teaching content to some extent) to satisfy the

(27% rural compared to 8% urban in general, with a

interests and constraints of the rural populations to be

proportion of over 35% of poor rural dwellers who have no

included in the school.

access to school); ii) those who cut short their primary

education before reaching the end of the cycle (for those

who have had access to school, 50% complete the cycle in

dwellers, under 40% of those who have started school

contribution to the family economy) and most traditional

The analyses show that these actions will most likely

not be enough for the poorest (children make a vital

rural areas compared to 74% in urban areas; for poor rural

(insufficient perception of benefits associated with “modern”

complete the cycle), thus contributing to maintaining

5. Conclusion

schooling) segments of the population. In these cases, the

It comes out of the analyses that the arrangements made

targeting of activities (generally new for education policy

for reducing the extra cost of small schools mainly concern

decision-makers) that directly concern the stimulation of

the utilization of personnel. Indeed, situations of high extra

demand should be envisaged.

costs occur when there is a high number of non-teaching

staff weighing on the fixed part of the cost function and

B) The second section of this study concerns the

when teachers are under-utilized in the course of normal

organization and cost of educational services in rural

school operation.

areas at lower secondary level. Improving schooling for rural children also involves providing them with the

today find it difficult to access secondary education upon

can nevertheless be highlighted: i) when a school has only

demonstrated that the perspective of secondary education

more than one person fulfilling this function, bearing in mind

As far as non-teaching staff are concerned, there is no

doubt that their functions are of importance, but two points

possibility of attending secondary education. Rural youth

120 or 150 students, there is certainly no need to have

completion of primary education, yet it has been

that ii) the functions of non-teaching personnel (contacts

increases the chances of primary survival. However, this is

with the administration, with parents, internal organization

above all important in the medium term insofar as it is clear

that progress in secondary enrollments will primarily

and teacher follow-up…) can also be distributed among

average in Sub-Saharan African countries, lower secondary

incomplete and who can in this way complete them and

those teachers whose service hours in the school are

concern the rural world. Indeed, it is calculated that, on

contribute to reducing the unit cost of services provided,

coverage is already relatively high in urban areas (66%),

with no negative impact on quality;

while it is relatively low in rural areas (22%); as rural areas

account for 70% on average of the lower secondary school-

age population, the conclusion is that as much as 85% of

As far as teachers are concerned, the reasons why

the improvement in lower secondary coverage, called for by

their hours of service are often lower than their theoretical

understand how important it is for countries to set up

them together. Five measures can be envisaged, either

hours relate to difficulties in organizing lessons and linking

all policy makers, will concern rural areas. One can then

separately or together, in order to remedy these

appropriate arrangements in order to ensure service quality

shortcomings:

and control the unit cost of these services.

The last point is indeed important, since in the current

situation, ministries of education often experience

review the hours of teaching time to students and the

hours of teacher service (these vary considerably from

one country to another) in order to make adjustments

difficulties in organizing these services at an acceptable

easier;

cost. Analyzing the cost of services in 21 countries in the

region has thus shown that a 120-student lower secondary school (the typical size for a local rural school) costs around

ii)

70% more on average per student than a 500-student

authorize (and pay appropriately) additional hours in order to reduce the use of teachers with only partial service;

school (a typical urban lower secondary school). This “extra

cost” of small schools obviously makes it difficult (costly) to scale up coverage for this level of education. The study

iii) introduce the rule whereby teachers not able to fulfill

immensely from one country to another, ranging from 10 to

administrative activities in order to make up their hours;

their complete service hours would have to carry out

shows that the “extra cost” of these small schools varies

160% within the sample of the 21 countries studied; some

iv) reorganize teaching programs in order to reduce the

countries therefore appear to more effectively contain the

number of subjects by grouping some subjects

cost of small schools than others, and can envisage scaling

together;

up enrollments with fewer constraints.

5. Conclusion

organize teacher training to avoid disciplinary areas

education system that exercises a strong influence on

cover a larger field.

Zimbabwe, the education system has less influence on this

being concentrated on one specific subject but rather to

social behavior, while in other countries, like Senegal or behavior. Three overall results stand out very sharply:

An analysis must be conducted for each country to identify

the

i) the feasibility of each of these options and ii) the manner

social impacts of education (for girls when they are

young then for women in adult life) are very substantial

to effectively implement them in each national context.

in rural areas where they are of greater intensity overall

C) Finally, the third section of this study concerns

than in urban areas. This result is partly connected to the

the social and economic impacts of education in rural

fact that the social behavior of illiterate women is

areas. The interest in addressing this aspect is two-fold: on

significantly more traditional in rural areas than in urban

Education is considered as being solely a right or else a

encourages women who have not attended school to

the one hand, it is generally not well documented.

areas, as if in towns, a sort of ambient modernity

public good, the value of which is so obvious that there is

adopt behavior that they would not have adopted in rural

no need for an empirical verification of its economic and

areas. However, the results obtained show that on

social impacts. However, it is necessary to make this

average, in rural areas, women’s education is an effective

verification in rural areas since, given their attachment to

vector of modern behavior and for improving social

traditions, it is important to know to what extent education

indicators.

can contribute to changing behavior and to adopting more

“modern” practices in the different social dimensions

spite of the strong impact of women’s education in rural

areas, this sometimes comes up against poorer

considered as important. On the other hand, measuring these impacts is also important for defining educational

availability of population and health services than in

differ from primary education to secondary education.

skilled attendance at childbirth, she has to seek out the

urban areas. For example, for a woman to benefit from

policy choices and priorities. The effects of education can

service (and tends to do so more if she is more

The study examines, in a separate and complementary

educated), but the service also has to be available within

(reduction of the risk of poverty, sustainable literacy in

the case in urban areas than in rural areas).

reasonable distance (and this is on average more often

manner, both the social impacts of educational investments adulthood, intergenerational effects on children’s schooling,

Primary

population variables, maternal health, child health including

the total social impacts of education. If we look at the

immunization and risk of infant mortality) and the economic

total impact obtained for people who have never attended

impacts in terms of employment and income.

education generates the highest proportion of

school and those who have benefited from tertiary

On the level of social impacts, there are marked

education, it can be determined as a whole for the different

differences are primarily to do with two general factors: i)

with a full course of primary education, while the additional

social dimensions taken into account that 52% is obtained

differences between the 21 countries analyzed; these

impact of lower secondary education is 24%, that of upper

the impact of education increases the capacity of educated

secondary is only 14% and that of tertiary education is

women to make better use of existing services. However,

limited to 9%. This result therefore highlights the essential

the effectiveness of health and population policies is far

need of ensuring that rural children can benefit from

from equal from one country to another. For example,

universal primary completion. It also highlights that it is

vaccination coverage exceeded 50% at the time of the surveys in Côte d’Ivoire, Rwanda and Zimbabwe, while it

useful to continue schooling through to lower secondary

countries, like Benin, Kenya and Mozambique, have an

benefits registered with primary education.

education insofar as this significantly reinforces the social

was only 20% in Mali, Uganda and Zambia; ii) some

5. Conclusion

On the economic level, that of employment and the

education, namely the measure of the standard of living

value added of education in productive sectors, the

or of wealth established on the basis of assets and living

analyses carried out show first of all the important

conditions on the one hand, and the monetary income of

contextual change affecting the structure of the working

farmers on the other. The first perspective indicates a

very different situations from country to country). During the

education. The second perspective indicates slightly

population in Sub-Saharan African countries (again with

substantial impact of education and particularly primary

last 20 years, the working population has increased

different results bearing in mind that this only takes into

evolution, but the structure has changed a lot at the same

production consumption, so important in traditional

considerably as a result of the earlier demographic

account the monetary aspect of farming income (own-

time. While the share of the working population employed in

contexts, is not taken into account). According to the

agriculture was estimated at 72% in 1985, it registered at

second perspective, it appears that primary education

just over 50% in 2003. At the same time, the share of

has virtually no impact on farming income in contexts

modern employment remained stable, at around 10% on

where agricultural employment represents a very large

is estimated to have risen from 23% to 42%. This evolution

and subsistence farming. On the other hand, in contexts

average, while that of non-agricultural informal employment

proportion of the working population, i.e. for traditional

therefore shows a pronounced trend in the reduction of the

where the proportion of agricultural employment is lower,

share of agricultural employment over time (although the

and where farming techniques are more diversified, then

total number of farmers in the region is still slightly on the

primary education makes a positive difference. This result

rise) and also a constrained trend in the development of

can be interpreted by saying that farmers who have

modern employment, the proportion of which has remained

completed primary education are undoubtedly more apt

basically stable on average over the last 20 years. In

to benefit from the possibilities provided by more modern

between these two “major” trends, non-agricultural informal

farming (when the country moves on from subsistence

employment, on a high quantitative increase, acts as an

farming, small surface areas and the use of the most

overall adjustment variable on the labor market. One of the

traditional techniques) than their illiterate counterparts.

consequences is that it is as if the informal sector included

on the one hand jobs producing “well-identified” goods and

This result suggests a structure of complementarity

services providing the jobholder with reasonable living

between the farmers’ education and the measures taken

intermittent and poorly-paid situations sometimes bordering

perceived as more conducive to the expression of the

If we now examine the value added of education in the rural

making more effective the activities which support the

whether we are talking about an agricultural job or non-

with the results obtained by Rosenzweig in his study on

conditions, and on the other hand jobs that are more or less

to promote rural development; thus, the context is

on unemployment.

impacts of education, bearing in mind that at the same

time the farmers’ education can also be perceived as

world, results are seen to differ somewhat depending on

modernization of agriculture. This type of result is in line India, who found high returns on education for farmers in

agricultural informal jobs (limited to those that offer a

States that had been concerned by the “green revolution”,

regular monetary income). Let us in any case highlight that education is not envisaged as a productive factor in itself,

while they were low in States that were not concerned.

productive in their work, and more specifically to help them

All of this is valid for primary education. For secondary

them to do their work more effectively.

significantly higher profitability of education for farmers

but as a factor liable to lead individuals to be more

education,

make better strategic and technological choices or enable For

the

analyses

carried

out

report

than with primary education, and this appears to be

agricultural jobs, two perspectives were adopted for

fairly reliable since it is clearly identified in all the

the reference variable in order to evaluate the impacts of

countries in the sample. Looking at this result

5. Conclusion

alongside that observed for primary education also

In all, and in order to offer a very synthetic summary of the

secondary school, farmers are capable of introducing

rural schooling is very real and must be filled at primary

suggests that, thanks to the knowledge they gain at

results of this study, it can be highlighted that i) the gap in

some elements of modernity to enable them to draw a

level first of all, and that this will no doubt require special

higher income whatever the circumstances (even if

measures, the usual ones having proven to be insufficient;

course possible).

even to pursue this effort at lower secondary level; iii) the

complementarity with rural development actions is of For

ii) there are economic and social reasons to do so, and

development of lower secondary education in rural areas

non-agricultural informal jobs in rural areas

will require a significant review of the ways of organizing

demonstrate that education is very profitable, again

(rural) schools. The analyses conducted show that this is

for primary.

the move towards greatly increased coverage of lower

(for those who have a regular income), results

educational services so as to contain unit costs in small

with higher value added for secondary education than

possible; that said, the issue of the overall sustainability of

secondary schooling should not be underestimated.

ÂŠ AFD Working Paper No. 94 â&#x20AC;˘ Developing Lower Secondary Education - September 2010 81

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Appendix 1.

A) Enrollments and social effects

List of Household Surveys Used in the Study

Country

Survey

Burkina Faso

Demographic and Health Survey

Cameroon

Demographic and Health Survey

Benin

Burundi

Central African Rep. Chad

Congo

Standardized Core Welfare Indicators Questionnaire

2003

Standardized Core Welfare Indicators Questionnaire

2002

General Census of Population and Housing Multiple Indicator Cluster Survey

2003 2004

2003

Standardized Core Welfare Indicators Questionnaire

2005

Multiple Indicator Cluster Survey

2001

Côte d’Ivoire

Demographic and Health Survey

Ethiopia

Demographic and Health Survey

DRC

Year

2005

Gambia

Multiple Indicator Cluster Survey

2000

Guinea

Demographic and Health Survey

2005

Kenya

Demographic and Health Survey

2003

Ghana

Guinea-Bissau Lesotho Malawi Mali

Mauritania

Mozambique Niger

Nigeria

Rwanda

Senegal

Sierra Leone Togo

Demographic and Health Survey

Standardized Core Welfare Indicators Questionnaire Demographic and Health Survey Demographic and Health Survey

Standardized Core Welfare Indicators Questionnaire Standardized Core Welfare Indicators Questionnaire Demographic and Health Survey Demographic and Health Survey Demographic and Health Survey Demographic and Health Survey Demographic and Health Survey Other Household Survey

Multiple Indicator Cluster Survey

2003

2002 2004 2004 2003 2005 2003 2006 2006 2005 2005

2003

2005

Uganda

Demographic and Health Survey

2006

Zimbabwe

Demographic and Health Survey

2006

Zambia

Demographic and Health Survey

2002

Appendix 1. List of Household Surveys Used in the Study

Labor market Country

Survey

Year

Benin

Core Welfare Indicators Questionnaire

2003

Burundi

Standardized Questionnaire on Development Indicators

2002

Burkina Faso

Core Welfare Indicators Questionnaire

Cameroon

Cameroon Household Survey

Chad

Chad Household and Informal Sector Survey

CAR

Congo

General Population and Housing Census

Standardized Questionnaire on Development Indicators

2002

2001

2003

2002 2005

Côte d’Ivoire

Household Standard of Living Survey

2002

Ghana

Core Welfare Indicators Questionnaire

2003

Guinea-Bissau

Core Welfare Indicators Questionnaire

2002

Madagascar

Household Survey

2001

Permanent Household Employment Survey

2004

Core Welfare Indicators Questionnaire

2002

Ethiopia Guinea

Lesotho

Malawi Mali

Mauritania

Mozambique Nigeria

Rwanda

Senegal

Sierra Leone Uganda

Zambia

Welfare Monitoring Survey Questionnaire Standardized Questionnaire on Core Welfare Indicators Core Welfare Indicators Questionnaire

Core Welfare Indicators Questionnaire

Standardized Questionnaire on Core Welfare Indicators Demographic and Health Survey

Standardized Questionnaire on Development Indicators Standardized Questionnaire on Development Indicators Integrated Household Survey

Socio-Economic Survey Questionnaire

Living Conditions Monitoring Survey III

2004

2002

2005

2006

2001 2001

2003

2002

92.4

46.3

73.7

55.9

94.6

43.8

68.2

87.7

89.6

68.2

Zimbabwe

75.5

97.8

98.1

80.7

81.1

96.9

89.6

98.3

95.8

85.4

65.8

93.1

97.6

88.9

67.2

87.6

79.8

57.8

95.7

95.8

94.0

69.1

66.1

89.2

80.7

73.9

94.4

72.4

57.1

68.2

94.8

73.0

44.3

79.2

72.9

97.4

85.3

97.9

88.3

76.8

55.3

93.4

95.5

79.1

47.6

81.1

69.6

42.6

94.9

97.9

93.8

41.4

49.7

82.5

73.4

67.1

93.0

67.1

45.9

46.3

88.0

65.1

31.6

67.9

91.6

97.4

97.6

100.0

92.1

83.3

93.8

98.7

95.3

90.3

93.7

92.9

81.7

100.0

96.9

91.6

86.7

94.8

83.9

95.4

99.3

74.5

77.4

83.4

100.0

92.0

83.8

80.1

68.6

94.9

79.8

93.2

84.9

74.5

54.5

94.2

94.5

68.3

43.4

75.2

67.9

43.1

92.8

94.1

88.9

40.4

42.8

78.3

71.1

52.9

92.5

62.4

31.2

38.5

84.9

55.1

30.9

64.9

84.3

98.1

94.2

99.7

98.1

86.5

77.2

93.0

99.2

93.8

60.8

89.8

85.4

68.3

97.5

100.0

99.6

73.8

72.3

95.8

84.0

81.0

96.1

75.3

63.6

67.3

99.6

73.0

42.9

80.1

Primary access Context Wealth R U Q12 Q345

45.5

88.0

58.3

55.5

61.3

61.1

28.2

25.5

82.6

75.9

9.1

14.1

36.3

32.7

38.2

77.1

67.5

22.9

33.9

76.9

51.3

21.4

57.4

38.7

16.8

32.0

73.8

18.0

22.7

41.1 25.6

50.4

85.9

67.2

62.3

75.8

59.0

36.4

23.9

83.0

82.2

15.0

22.5

42.4

45.2

49.5

48.6

69.5

27.2

45.7

76.2

57.6

23.4

68.0

48.5

27.2

45.8

71.1

21.0

36.9

83.8

45.1

54.3

59.8

40.9

18.6

20.1

62.7

72.9

4.2

8.1

23.1

24.8

36.3

58.4

61.3

5.8

24.5

69.4

35.2

11.5

51.9

40.8

12.1

19.4

54.4

17.2

14.8

38.7

68.2

91.8

89.8

74.5

84.0

77.0

45.6

43.2

93.3

90.6

36.1

28.8

60.4

66.1

66.8

87.3

46.9

67.4

85.9

73.5

58.2

82.4

47.9

52.8

65.1

86.7

60.8

64.1

62.6

29.9

79.2

29.0

29.8

52.5

33.4

15.3

14.7

59.1

59.7

4.6

5.0

18.3

23.5

24.7

41.7

49.6

4.3

15.1

61.3

36.3

6.4

46.8

32.4

4.6

14.7

42.7

8.8

12.8

39.7

55.3

90.2

74.2

64.6

80.3

63.7

40.6

28.3

92.5

89.4

10.9

17.1

49.3

55.4

51.4

75.1

79.9

32.3

50.3

88.0

68.1

22.8

67.4

47.9

26.3

42.0

86.4

24.5

26.7

58.7

Primary completion Context Wealth R U Q12 Q345

55.2

Gender G B

Source: compiled by the authors based on household surveys listed in Appendix 1.

Average 29 countries

Zambia

Uganda

Togo

Sierra Leone

97.0

93.8

Senegal

Rwanda

80.7

Republic Congo

Niger

Nigeria

77.5

83.0

48.7

Mozambique

Mauritania

Malawi

95.7

Mali

100.0

57.5

85.5

Lesotho

Kenya

Guinea

Guinea Bissau

95.9

67.6

Gambia

Ghana

Ethiopia

67.4

Democratic Congo

Chad

Côte d’Ivoire

53.6

59.9

34.6

62.7

Gender G B

% Rural

Central African Rep

Cameroon

Burundi

Burkina Faso

Benin

Point in the system Group of population

35.4

74.2

36.2

35.4

50.2

53.7

23.6

11.3

74.9

58.9

7.5

10.2

29.9

27.0

29.2

51.8

34.4

14.5

29.2

69.5

46.7

16.9

49.2

36.1

11.5

23.1

61.1

12.6

17.6

31.5 20.0

41.5

74.2

45.6

47.7

66.3

50.7

31.0

10.8

74.7

67.2

12.3

17.9

35.5

40.7

36.3

34.3

46.4

20.3

40.9

70.5

50.8

17.8

60.1

46.1

22.2

34.0

62.5

13.2

27.2

70.5

25.2

35.0

48.7

31.5

13.9

6.8

52.0

54.2

2.9

4.9

17.2

20.4

24.9

37.7

32.4

3.7

20.0

60.6

27.7

7.0

41.9

37.9

8.6

10.4

42.2

10.9

10.8

30.5

58.7

80.2

64.8

74.5

74.4

69.4

39.6

27.6

86.8

79.9

32.0

23.4

52.6

58.8

56.4

70.3

59.2

33.4

61.4

81.2

68.9

49.5

76.9

45.8

43.1

52.2

76.8

49.1

51.9

62.6

20.2

56.0

13.8

15.6

41.6

24.5

11.7

4.5

45.6

38.6

2.2

1.9

12.0

19.6

14.6

20.4

24.2

2.8

11.5

51.6

27.3

3.2

37.3

25.6

3.4

7.8

29.9

5.0

7.8

27.0

54.5

45.2

80.3

48.1

46.8

70.1

54.3

34.4

11.1

87.0

76.0

9.0

12.8

41.2

48.9

37.9

55.8

47.9

21.9

44.5

82.9

64.7

14.1

59.0

46.2

19.7

28.7

76.1

15.4

20.9

Secondary 1 access Context Wealth R U Q12 Q345

52.4

Gender G B

Table A1. Education profile per country for primary and lower secondary education according to gender, geographical context and level of family wealth

24.7

56.8

32.1

17.5

29.6

41.5

13.7

8.7

43.0

53.8

4.2

23.2

16.0

26.0

29.8

31.2

6.3

24.6

60.9

39.9

15.8

40.0

23.3

5.8

11.0

30.5

8.0

8.5

16.0

29.8

61.2

40.0

28.7

41.4

40.9

18.1

8.8

47.1

59.8

7.7

25.8

28.6

33.4

24.2

44.9

10.4

33.2

61.5

43.6

15.0

51.3

28.8

12.6

18.8

28.3

8.0

11.7

30.2

16.7

53.7

20.4

17.3

19.0

19.2

5.0

4.8

19.7

47.4

1.2

11.9

10.1

21.3

21.8

30.1

0.9

14.5

48.6

21.7

5.2

31.5

18.9

3.5

3.2

10.3

5.9

4.3

13.5

58.2

44.2

65.4

59.5

50.1

52.6

57.6

24.5

23.9

74.3

10.1

40.4

41.4

54.9

53.1

56.0

16.8

51.6

74.9

61.4

46.4

70.2

32.1

29.1

31.0

40.6

39.8

29.6

36.3

11.0

30.4

9.9

4.7

14.1

17.3

3.6

2.7

16.8

30.2

0.3

7.3

9.9

11.1

9.3

20.8

0.3

6.1

36.8

20.7

0.9

26.8

10.3

2.3

2.1

5.4

1.4

2.5

15.7

32.2

65.1

41.8

25.9

43.5

39.3

21.2

6.9

56.5

70.5

4.7

30.9

34.4

34.5

36.3

45.8

10.3

36.1

75.9

56.9

10.8

49.9

28.8

8.7

13.6

37.0

10.0

11.4

28.2

Secondary 1 completion Gender Context Wealth G B R U Q12 Q345

Appendix 2. Related Tables

28 32

Students

88 2

Students

28 38

Total

Students

Pedagogical groups

History-Geography

Language 1

120

112

120

112

120

112

Number of teaching hours needed / week 1 2 3 4 Total

Maths-Physics-Chemistry

Language 0

3 - Subject regrouping – 120-student school Years

Pedagogical groups

Total

Geography

History

Language 1

Number of teaching hours needed / week 1 2 3 4 Total

Chemistry-Biology

Physics

Maths

Language 0

2 - Overtime – 120-student school Years

Pedagogical groups

Total

Geography

History

Language 1

Number of teaching hours needed / week 1 2 3 4 Total

Chemistry-Biology

Physics

Maths

Language 0

1 – Gross – 120-student school Years

Table A2: Unit cost in a 120-student lower secondary school with a variety of organization modes

Service

1.0

STR

Teachers Number

STR

8.00

1.0

Teachers Number

STR

Teachers Number

1.33

15.0

0.78

0.67

0.89

0.81

0.67

% utilization

15.0

0.78

0.67

0.44

0.89

0.44

0.67

1.33

% utilization

12.0

0.62

0.67

0.44

0.89

0.44

0.67

% utilization

Teacher hours Available overtime

12 000

9 833

9 000

Non Salary Teacher expend.

75.0

Unit Cost

81.9

Unit Cost

100.0

Unit Cost

Appendix 2. Related Tables

28 38

Total

Students

89 28 150

Students

Source: compiled by the authors.

Pedagogical groups

Total

Geography

History

Language 1

140

110

100

500

280

120

112

Number of teaching hours needed / week 1 2 3 4 Total

Chemistry-Biology

Physics

Maths

Language 0

1 – Standard – 500-student school Years

Pedagogical groups

History-Geography

Language 1

Number of teaching hours needed / week 1 2 3 4 Total

Maths-Physics-Chemistry

Language 0

4 – Subject regrouping + overtime – 120-student school Years

Service

STR

Teachers Number

STR

Teachers Number

23,8

0.74

0.83

0.56

0.74

0.56

0.83

% utilization

20.0

1.04

0.67

0.89

1.22

1.33

% utilization

Teacher hours Available overtime

7 972

24 000

Non Salary Teacher expend.

48.0

Unit Cost

66.4

Unit Cost

Appendix 2. Related Tables

Série Documents de travail / Working Papers Series Publiés depuis janvier 2009 / Published since January 2009 Les numéros antérieurs sont consultables sur le site : http://recherche.afd.fr Previous publications can be consulted online at: http://recherche.afd.fr

N° 78

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