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Raising primary school enrolment in

developing countries

The relative importance of supply and demand

Sudhanshu Handa

Inter-American Development Bank, 1300 New York Avenue, Northwest, Washington, DC 20577, USA

Received 1 June 1999; accepted 1 August 2001

Abstract

Few policies are as universally accepted as raising primary school enrolment in developing

countries, but the policy levers for achieving this goal are not straightforward. This paper merges

household survey data with detailed school supply characteristics from official sources, in order to

estimate the relative impact of demand and supply side determinants of rural primary school

enrolment in Mozambique. Policy simulations based on a set of ‘plausible’ interventions show that in

rural Mozambique, building more schools or raising adult literacy will have a larger impact on

primary school enrolment rates than interventions that raise household income. When relative costs

are considered, adult literacy campaigns are nearly 10 times more cost-effective than the income

intervention and 1.5 to 2.5 times better than building more schools.

D 2002 Elsevier Science B.V. All rights reserved.

JEL classification: D1; I0; I2; O1

Keywords: Primary education; School supply; Africa

1. Introduction

Few policies, if any, are as universally accepted as that of raising primary school

enrolment in poor countries. Virtually every World Development Report published

annually by the World Bank has recognized, in one form or another, the importance of

primary schooling as an input to the social and economic progress of poor countries.1 And

0304-3878/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved.

PII: S0304 -3878 (02 )00055 -X

E-mail address: [email protected] (S. Handa).1 Within the overall policy goal of raising primary school enrolment, raising girls’ enrolment has received

special attention, due to the large positive externalities of female education on children and adult health, fertility,

and infant mortality.

www.elsevier.com/locate/econbase

Journal of Development Economics 69 (2002) 103–128

within the academic literature, a host of studies has documented the market and nonmarket

return that comes from completing primary schooling, both in poor and rich countries

alike.2

However, raising primary school enrolment in developing countries is easier said than

done. The relative importance of school supply versus household demand factors remains

controversial, with serious implications for education policy.3 For example, if children’s

enrolment rates are not responsive to local school infrastructure, government interventions

aimed at increasing access to schools will have very limited impact on overall schooling

levels, thus effectively reducing the set of options available to policymakers. And even if

regional variations in schooling infrastructure can be related to household schooling

choices, as several studies have shown,4 efficient policy decisions require knowledge of

the particular dimensions of school infrastructure that matter most. This latter issue is

contentious in both developing and developed countries alike, and has been the topic of

several recent articles seeking to measure the type of schooling infrastructure (access,

quality, etc.) that makes a difference for household schooling choices.5

This study makes three main contributions to the literature on primary school enrolment

policies and school infrastructure in developing countries. First, the impact of school

characteristics on household primary school enrolment decisions are measured using a

diverse set of school ‘quality’ indicators. Aside from information on distance to the nearest

school, detailed information on school characteristics is hard to find in developing

countries, and as a result, the available published literature is small relative to that for

developed countries.6 This study thus provides an additional set of estimates with which to

assess the role of specific supply side factors in determining student outcomes. Moreover,

school characteristics are measured with the actual data that Mozambique’s Ministry of

Education uses to formulate its regional and national targets, and to develop its 5-year

plans, thus enhancing the policy relevance of the work. Second, unlike most previous

studies in this area, the interaction between school and household characteristics is

explored to see if complementarity or substitutability exists between these two sets of

factors in determining school enrolment.7 The existence of significant interactions can

provide important clues about who benefits the most from school supply interventions, and

3 See Simmons and Alexander (1978) for a discussion of this issue and review of the literature.4 These studies show that community or regional fixed effects are significant determinants of household

schooling choices. For example, see Pradhan (1998) for Indonesia, Handa (1996) for Jamaica, and Alderman et al.

(1996) for Pakistan.5 Recent studies that measure the effect of various school characteristics in developing countries include Lavy

(1996) and Glewwe et al. (1995); for developed countries, see Card and Kruger (1992), Betts (1995), and

Golhaber and Brewer (1997). The overall importance of school quality is discussed by Hedges et al. (1994),

Hanushek (1995), and Kremer (1995).6 Recent studies that provide estimates of detailed school ‘quality’ indicators on student educational

achievement in developing countries include Glewwe and Jacoby (1994) for Ghana, Glewwe et al. (1995) for

Jamaica, and Tan et al. (1997) for the Philippines.7 Birdsall (1985) is one of the few studies that also looks at interactions among supply and demand factors in

determining schooling outcomes.

2 For developing countries see Glewwe (1999), Handa (1999), and Lam and Duryea (1999). For developed

countries, see Rosenzweig and Schultz (1982).

S. Handa / Journal of Development Economics 69 (2002) 103–128104

where programs or resources should be placed in order to benefit the most vulnerable. For

example, a significant positive interaction between household income and school access in

a particular region implies that building additional schools in this region will benefit richer

households more than poorer ones. However, policymakers may want to target regions

where the poor are more likely to benefit from the provision of schools, hence, knowledge

of specific interactions can provide useful information to help prioritize program place-

ment.

Third, policy simulations are presented based on separate and ‘plausible’ supply and

demand side interventions, and used to evaluate which type of intervention will have the

largest impact on primary school enrolment rates. The standard policy analysis contained

in studies on school supply typically evaluates the effect on the outcome of interest (for

example, test scores or grade attainment) for a given change in a statistically significant

school supply variable (e.g. travel time to school) by multiplying the given change by the

relevant coefficient. Although statistically valid, the policy relevance of this exercise can

be enhanced significantly by recognizing that governments in the short or medium run

cannot supply 10 more books to every school, or fix every leaking roof in the country. A

typical policy intervention in the short run will involve adding more teachers in some

regions and not others, or building a few schools in a few regions. This paper estimates the

change in primary school enrolment that would come about from a set of more realistic

interventions such as building a few schools in specific regions, or targeting adult

education or income-generating programs only among the poorest households. These

simulations arguably provide a better picture of the expected benefits of the type of

interventions available to developing country governments in the short and medium term.

The data used in this paper are from Mozambique, a country that has suffered from over

25 years of armed conflict, and that is acknowledged by development experts as one of the

world’s poorest. Estimates from the rural region of this country show that both demand

and supply factors are important determinants of primary school enrolment. On the supply

side, dimensions of school quality (the number of trained teachers) and access all have

significant effects on household enrolment decisions. However, the policy simulations

show that school access on the supply side, and adult education on the demand side, are

the biggest factors in impeding primary school enrolment. When relative costs are

considered, interventions that raise adult literacy turn out to be the most efficient

alternative for raising primary school enrolment in rural Mozambique.

2. Data and description of study area

2.1. The study area

Mozambique is one of only a handful of African countries that was colonized by the

Portuguese, and by all accounts, the period of colonization was extremely repressive for

native Africans. Only a select few assimilados were allowed access to the social and

economic benefits that the colonial rulers enjoyed, and independence came in 1975 only

after a long war of independence and a change in government in Portugal. Almost

immediately after independence, the new Mozambique entered an even more brutal civil

S. Handa / Journal of Development Economics 69 (2002) 103–128 105

war, instigated by guerrillas backed by neighboring South Africa. This war of destabiliza-

tion resulted in thousands of land mines being placed in rural areas in the central and

northern parts of the country. Hundreds of thousands of Mozambicans fled the countryside

for the urban centers or neighboring Malawi and Zimbabwe. The signing of the peace

treaty in Rome in 1992 essentially marked the second independence of Mozambique, but

the over 25 years of armed conflict have left a huge hole in social and economic

infrastructure that require immediate attention in order for Mozambique to realize

sustainable growth and reduce poverty. A recent study by the Ministry of Finance

(1998) estimates that 70% of the population live below the poverty line, with poverty

rates even higher in the central and northern zones that suffered most from the civil war.

2.2. The national education system

The national education system’s general education program is divided into two levels—

primary and secondary. Primary education consists of 7 years of schooling divided into

two levels, Level 1 up to Grade 5 (escola primaria do primeiro grau or EP1) and Level 2

from Grade 6 to 7 (escola primaria do segundo grau or EP2). Secondary education

consists of 5 years also divided into two levels or cycles, first cycle secondary from Grade

8 to 10 (escola secundaria geral do primeiro grau or ESG1) and second cycle secondary

from Grade 11 to 12 (escola secundaria geral do segundo grau or ESG2).

Unlike most African countries, entrance into successively higher levels of schooling is

not based on national examinations, but on actual grades and age. Among students with

the same grades, those who are younger (and therefore either started on time or did not

repeat as often) are given priority. Access to EP1 in rural areas, and other (higher) levels

through out the country, is supply constrained. Fees do not exist in public lower primary

schools, but there is an annual matriculation fee of approximately US$5. Private EP1

school fees can range from US$150 to US$600 per year depending on ownership structure

and facilities provided.

2.3. Household data

The household data used in this paper come from the first post-war national household

survey of Mozambique undertaken in 1996/1997 by the National Statistical Institute—the

Inquerito Nacional Aos Agregados Familiares Sobre As Condic�oes de Vida (IAF). The

IAF is a multipurpose household survey that contains detailed information on consumption

expenditures, as well as modules on health (both adult and child), education, employment,

demographic composition, and a community questionnaire for rural areas describing local

infrastructure.8

The IAF is a three-staged stratified sample. Stage 1 is the 11 provinces of the country,

Stage 2 is the localidad (bairro in urban areas), and in Stage 3, households are selected

8 This data set has been used by the International Food Policy Research Institute (IFPRI) in collaboration

with the Mozambican Ministry of Finance to construct a national poverty line and to develop a poverty profile of

Mozambique (Ministry of Finance, 1998).


from villages (or blocks in urban areas). The primary sampling unit is therefore the

localidad, and variance estimates provided in this paper account for the sample design of

the survey. The full survey covers approximately 42,000 individuals residing in 8250

households.

Tables 1 and 2 provide basic indicators of adult and child schooling calculated from the

IAF data set. Only 40% of adults aged 18–65 can read or write; for women, the literacy

rate is even lower (24%). Note that women in rural areas have the lowest literacy rates—

16% for the 18–65 age group. Table 2 indicates that there are significant signs of

improvement. The net enrolment rate for primary school is 49%, and is slightly lower for

girls (45%) and rural children (44%).

2.4. School data

Information on school infrastructure in the IAF is limited to whether or not a rural

village contains a primary school. Detailed information on school characteristics has been

gathered from the Direcc�ao de Planificac�ao of the Mozambique Ministry of Education

(MINED). Since 1992 MINED has administered a beginning and end-of-academic-year

questionnaire to each school in the country, soliciting information on enrolment, teachers,

teacher qualifications, pass rates, and building characteristics. This information is used by

MINED to create and keep track of its internal performance indicators. Coverage is

excellent, with over 90% of schools returning questionnaires; summaries of these data are

published in an annual report by the MINED entitled Educational Indicators (Republic of

Mozambique, various years).

Raw data from these school surveys for 1995 and 1996 were acquired from MINED

and were merged at the administrative post level with rural households from the IAF

survey.9 The analysis presented below focuses on the enrolment decision of rural children

(representing 80% of the primary school age children in Mozambique) in order to exploit

the small information on rural village level schooling availability provided in the IAF; in

all there are 634 villages in the IAF, distributed across 175 administrative posts, 112

districts, and the 10 provinces of the country (excluding the province of Maputo City).10

Table 1

Adult literacy rates by age group (%)

18–65 years 66–99 years

Rural Urban Mozambique Rural Urban Mozambique

All 32.0 71.0 40.0 29.7 69.2 37.7

Male 52.3 85.1 59.3 42.9 78.4 50.3

Female 15.7 57.6 23.6 17.5 60.2 26.0

Poor 31.2 61.8 36.6 28.4 59.1 34.2

10 In 17 cases, MINED did not have any school information for an administrative post found in the IAF. In

these cases, school information from a bordering administrative post was used.

9 There are three levels of local administration in Mozambique: province, district, and administrative post.

The school level data are therefore aggregated to the lowest administrative unit possible.


2.5. Choice of school characteristics

MINED divides its educational performance indicators into three groups, measuring

coverage, quality, and efficiency of the school system, and I follow this classification

where appropriate in order for the results to be of policy relevance to the Government of

Mozambique.11 MINED has developed a set of indicators to measure each of the three

dimensions of the educational system—where possible, I used these same indicators in the

regression analysis, although there is a high degree of collinearity among the indicators,

both across and within the three dimensions of coverage, quality, and efficiency.

The basic quality indicators used by MINED are the number of trained teachers

working in the system, average class size, and the pupil– teacher ratio. The number of

trained teachers in the administrative post was used as the basic indicator of teacher

quality. However, I also find that gender of the teachers matters, and so show some results

that measure the proportion of female trained teachers in the administrative post. In

addition to teacher training, the average pupil– teacher ratio for schools in the admin-

istrative post was included. Class size is not used because many schools in Mozambique

are run on a shift system, and so smaller class sizes can be achieved by creating two shifts,

but with only a small number of additional teachers (Case and Deaton, 1999 report the

same phenomenon for South Africa).

School coverage is measured by the number of Level 1 primary schools (EP1) in the

administrative post. Given the large variations in the building structure of schools in

Mozambique, and evidence from other developing countries on the importance of building

characteristics (e.g. Glewwe and Jacoby, 1994), the number of school rooms made of

cement in the administrative post was also included. All these school supply variables are

measured at the administrative post level, so each household in the administrative post will

have the same school infrastructure characteristics. Also included is the (log of) travel time

to the nearest EP1 school to the village, taken from the IAF community questionnaire—

this controls for the very important travel time cost component of school attendance and

also allows for some village variation in school infrastructure. As in other sub-Saharan

African countries, girls’ schooling rates lag behind those of boys in Mozambique and are

thus of particular policy importance. I allow the impact of all school infrastructure

variables to differ by gender, estimating separate models for boys and girls.

11 I do not look at the impact of schooling efficiency, defined by MINED as the pass rate, since this can also

be interpreted as a school outcome indicator and not an input indicator.

Table 2

Children’s current enrolment by age group (%)

7–11 years 12–17 years

Rural Urban Mozambique Rural Urban Mozambique

All 43.9 70.7 49.2 43.3 63.5 48.0

Male 49.1 73.5 53.9 51.5 65.6 54.5

Female 39.0 68.0 44.7 33.2 61.4 40.3

Poor 41.7 63.3 45.5 42.3 54.9 44.8


Table 3 provides means of the school supply variables used in the regression analysis.

These means are calculated over the 175 administrative posts found in the rural sample of

the IAF, and show that the mean number of EP1 schools is 21, with an average of one

cement room per school. Only 59% of the administrative posts have a Level 2 primary

school (EP2) school, and only 20% have a secondary school.

3. Econometric model, sample, and results

3.1. Econometric model and sample

The impact of school characteristics on household schooling decisions is measured via

reduced form demand equations for children’s schooling of the form

Si ¼ FðXc;Xh;Xs; uÞ ð1Þ

where Xc are characteristics of the individual child (age), Xh are household characteristics

that capture access to resources, differences in taste for schooling, and opportunity costs,

Xs is the vector of school infrastructural characteristics discussed above, and u is a random

error term with the usual assumptions.12 The household level variables included in the

model are the age and sex of the head, whether the head is literate, whether any adult

household member has completed Grade 7 (EP2), and whether any adult female has

completed Grade 5 (EP1). Household resources are measured with per capita daily

Table 3

Means for administrative post school characteristics

Mean

Coverage or access indicators

No. of EP1 schools 21

No. of cement rooms 22

EP2 school exists 0.59

Secondary school exists 0.20

Quality indicators

No. of trained teachers 66

No. of female trained teachers/total number of teachers 0.08

No. of female teachers/total number of teachers 0.37

No. of trained female teachers/total number of female teachers 0.15

Efficiency indicators

Overall pass rate 0.64

Female pass rate 0.57

Male pass rate 0.68

Portuguese pass rate 0.66

Mathematics pass rate 0.68

Data taken from survey of schools conducted by the Ministry of Education.

12 See Strauss and Thomas (1995) for a review of this methodology.


expenditure on all goods and services including home production. This is treated as

endogenous following the recommendation of Rivers and Vuong (1988), using the cluster

median expenditure for identification.13 Also included are measures of farm assets and

production such as total land holdings, access to irrigation, agricultural commercialization,

and provincial dummy variables.

For Mozambique (and most African countries), raising primary school enrolment rates

is a priority and the focus is therefore on the analysis of school supply effects on the

primary school enrolment decisions of rural households. The sample is children of primary

school age (7–11 years old), and the dependent variable is whether the child was currently

enrolled in school at the time of the survey. Means for these variables are presented in

Table A1 of the Appendix A.

3.2. Placement of school infrastructure

The analysis of the impact of school infrastructure on school enrolment runs the risk of

confounding cause and effect if households with a greater preference for schooling are able

to move to areas with better schooling quality. In the United States, for example,

households demonstrate preference for schooling quality through higher property prices

in districts with better schools. In Mozambique and other poor countries, allocation of

infrastructure such as school or health services may be influenced by local demand for

services. In such cases, regression estimates that do not account for endogenous program

placement will overstate the impact of school characteristics on household educational

choices. On the other hand, if policymakers purposely place programs in regions where

school outcomes are low, then standard regression estimates will lead to underestimates of

the true program effect.

Mozambique’s history of armed conflict led to destruction of physical infrastructure

including schools, roads, and health centers, and formal provision of educational centers

by the state was limited to the southern part of the country and to mostly urban zones.

During this period, very few new schools were constructed, and some were constructed

through community initiatives, which would reflect community preferences for schooling.

Since the peace accord in 1991 and the general elections of 1994, there has been a rapid

increase in the number of schools constructed in the rural areas, both due to Government

and NGO interventions. This is corroborated by the IAF community survey, which reports

that nearly half of all primary schools were built after 1992.

The education budget is distributed among the provincial directorates of education, who

allocate resources to its districts based on planning and need as articulated by the district

education directorates. In discussions with staff at the National Planning Directorate of

MINED, considerable scepticism was displayed about the ability of parents and others at

the village level to influence school placement and quality. This feeling was also expressed

by primary school teachers interviewed by the author in urban Maputo, who felt that

13 Household consumption decisions also affect leisure consumption, and are made jointly with schooling

decisions. Due to this simultaneity problem, median per capita consumption of the village is used to instrument

household consumption. This variable is highly correlated with household consumption: the simple OLS

coefficient of log (consumption) on log (cluster median consumption) is 0.88.


parents had very little influence on how schools were run or how resources were allocated.

Presumably, this would be even more so in rural areas which are poorer and where families

are more dispersed.

As mentioned earlier, there has been considerable rehabilitation of social infrastructure

in rural Mozambique since the General Peace Accord in 1991. In the IAF sample of 634

villages, 68% report having a primary school. Of those schools that report a date of

construction (82% of cases), 42% indicate that the school was built after the signing of the

peace treaty. What determined the placement of these relatively ‘new’ schools in rural

areas of Mozambique? To evaluate the extent to which endogenous placement might bias

the estimates of program effects, the average characteristics of villages with a ‘recent’

school (i.e. built since the war) and villages without a school were compared to see if

village characteristics are sufficient to explain program placement.

I constructed program exposure time as (1997� t) where t is the date the school was

built in the village. The exposure time of villages who had a school built in the year of the

survey (1996), for example, is thus 1, while villages without a school are given an

exposure time of 0, and the resulting variable is regressed on a set of village level variables

including median village consumption expenditure, the proportion of household heads that

are literate, the proportion of households with an adult with EP2, and the proportion of

households with a female adult who has completed EP1. Since geographic location is often

an important determination of program placement, I also included the distance (km) to the

district capital, and the distance to the nearest ‘good’ road.

Ordered probit estimates of the village level determinants of program exposure are

presented in Table 4. Column 1 shows that none of the village level SES variables or the

distance variables are able to predict school placement since Mozambique’s reconstruc-

tion. Column 2 of Table 2 adds provincial dummy variables to the equation, and these

results show some significant regional variation in program placement, but the village

level characteristics remain insignificant in determining placement.

Another way that parents can influence programs is by demanding better quality.

Among recently constructed schools, are there systematic quality differences that vary by

household characteristics? To answer this question, we must go up to the administrative

post level, which is the lowest level at which school quality information can be merged

with IAF villages. Now, only those administrative posts that contain a recently (since

1991) constructed school are selected and checked to see whether school quality, measured

by the average pupil–teacher ratio and the average proportion of teachers with training,

varies according to the socioeconomic status of households in the administrative post.14

The socioeconomic variables are the same as those used earlier for the village level

analysis, and since the level of aggregation is higher, the average distance (of villages in

the administrative post) to the provincial capital was used to capture geographic targeting.

Results of this analysis are presented in Table 5, and for either measure of school

quality, the F test at the bottom of the table fails to reject the null hypothesis that the set of

14 It is important to realize that I cannot do this for all administrative posts. Administrative posts that have

had schools for many years (high exposure) will probably also have higher rates of adult literacy and primary

school completion, leading to a positive correlation between school placement or quality, and household

socioeconomic status.


SES variables are jointly equal to 0. There is some indication (at the 10% significance

level) that median consumption in the administrative post is negatively correlated with the

pupil–teacher ratio (the higher the median consumption, the worse the ratio), while there

continues to be significant variation across provinces in school quality.

Another way to assess the randomness of placement rules is through a difference-in-

differences approach that uses cohorts to construct control groups, as in Duflo (1999). Since

there was a major expansion in school infrastructure after 1993, children of primary school

age before 1993 faced very different school infrastructure availability relative to their

younger brothers and sisters. Children aged 14–17 in the IAF would have been 8–11 in

1990, just before the revitalization of school infrastructure in the country, and hence, not

subject to the ‘program’. I therefore used this cohort as a control group, and constructed a

difference-in-differences estimate by comparing the difference in school outcomes between

cohorts across administrative posts with large and small increases in schools. I compared the

1993 round of the MINED data with the 1996 round, and defined administrative posts with

‘high exposure’ as those that had more than the median number of new schools built during

this period, and those with less than the median as low exposure (the median is 5).

Table 6 provides the results of this exercise for two schooling indicators: enrolment and

grade attainment adjusted for age. For enrolment, the simple cross-sectional difference in

enrolment rates, that is, the difference among 7–11-year olds across low- and high-

exposure regions is 0.099, while the difference-in-differences is 0.09, almost identical.

Moreover, the pre-program difference (the difference among the control group across

Table 4

Ordered probit estimates of years since primary school built in village

(1) (2)

Coefficient z-statistic Coefficient z-statistic

Median village consumption 0.000 1.12 0.000 0.20

Proportion of heads literate 0.037 0.14 0.012 0.04

Proportion of households

with female with EP1

0.017 0.03 0.279 0.47

Proportion Of households

with adult with EP2

0.129 0.22 � 0.035 0.06

Distance to ‘good’ road � 0.001 0.84 � 0.001 0.50

Distance to district capital � 0.003 0.60 � 0.008 1.59

Niassa 0.616 1.44

Cabo Delgado 1.397 3.03

Nampula 0.964 2.44

Zambezia 0.518 1.32

Tete 0.646 1.55

Manica 1.102 2.67

Sofala 0.771 1.74

Inhambane 0.312 0.75

Gaza 1.244 2.85

Log likelihood � 380 � 374

Observations (villages) 266

Sample is villages that have no school or that had one built after 1991. Dependent variable is equal to 0 if village

has no school, and equal to (1997� t) if has school, where t is the year the school was built.


regions) is not statistically significant. The last part of Table 6 repeats the exercise using

another outcome indicator, standardized or adjusted grade attainment, defined as the

highest grade attained by the child divided by the ideal grade s/he should have attained

given age.15 In this case, the cross-sectional difference (0.0740) is over twice that of the

difference-in-differences (0.033), but even for this outcome, the pre-program difference is

not statistically significant.

In conclusion, based on the discussion with administrators in the Mozambican MINED,

as well as the results on the determinants of placement and quality of new schools in rural

areas, it appears that the program effects estimated below are unlikely to represent simply

unobserved household or community level demands for schooling.

3.3. Basic results

3.3.1. Results on school access or coverage of the educational system

Table 7a presents marginal probability estimates of the impact of school access on

EP1 enrolment by gender in rural Mozambique. Columns 1–3 show estimates using

Table 5

OLS estimates of determinants of school quality at administrative post level

Dependent variable Proportion of teachers with training Pupil– teacher ratio

Coefficient z-statistic Coefficient z-statistic

Proportion of heads literate � 0.043 0.41 � 11.744 1.34


with adult with EP2

� 0.139 0.68 � 13.767 0.81


with female with EP1

0.197 1.21 21.388 1.57

Median consumption of

administrative post

0.000 0.02 0.002 1.74

Distance to Provincial capital 0.000 0.61 0.034 1.46

Niassa 0.090 1.19 � 28.619 4.54

Cabo Delgado 0.232 2.77 � 36.078 5.16

Nampula 0.294 4.09 � 25.574 4.27

Zambezia 0.188 2.69 � 0.879 0.15

Tete 0.218 2.79 � 19.038 2.92

Manica 0.259 3.56 � 23.375 3.85

Sofala 0.340 4.05 � 14.642 2.10

Inhambane 0.149 2.07 � 9.368 1.56

Gaza 0.061 0.77 6.096 0.91

Constant 0.553 6.77 72.452 10.64

R2 0.30 0.60

F 2.63 9.17

P value for SES variables 0.82 0.17

Observations

(administrative posts)

102

Sample is rural administrative posts with at least one school built after 1991. All variables are measured at the

administrative post level, except for province dummies.

15 For children out of school, the IAF reports their age when they finished school.


the number of schools in the administrative post, while columns 4–6 show estimates

using the change in the number of schools between 1993 and 1996. The number of

schools in the administrative post has a significant effect on boys but not girls

enrolment ( p value for difference in effects is 0.04), while the number of cement

rooms has a significant effect on girls’ but not boys’ enrolment ( p value for difference

is 0.08). Proximity to a school is a highly significant determinant of enrolment, the

point estimates implying that a 30-min reduction in travel time would increase

enrolment probabilities by 20 and 17 percentage points for boys and girls, respectively.

When the change in the number of schools is used instead of levels, the main

difference is the impact of schools on girls’ enrolment, which now increases and

becomes significant at 10%.

Household characteristics are also important determinants of school enrolment in rural

Mozambique, particularly for girls. In columns 1–3, for example, all the adult schooling

variables are statistically significant, and in all cases, the marginal impact of additional

adult schooling is larger for girls than it is for boys; the existence of a female adult with

completed EP1 is especially important for girls, raising enrolment probabilities by 21

percentage points. Household access to resources (measured by per capita consumption

expenditures) also influences enrolment rates, and in this case as well, the impact is larger

for girls than it is for boys.

3.3.2. Difference-in-differences estimates of school access

The difference-in-differences analysis using older cohorts as controls can also be

applied in a multivariate context in order to provide a check on the cross-sectional

estimates presented above. Consider the following regression equation estimated over all

children ages 7–17:

E ¼ a0 þ B1*X1 þ B2*ðCohortÞ þ B3*ðProgramÞ þ B4*ðCohort*ProgramÞþ ui ð2Þ

In this framework, X1 is a vector of control variables, B2 measures the difference in

enrolment rates between older and younger cohorts in APs without any significant change

Table 6

Change in schooling outcomes across cohorts and by intensity of schooling construction

Schooling outcome Enrolment Standardized grade attainment

No increase Large increase No increase Large increase

Control 0.407 0.416 0.292 0.333

(ages 14–17)

(N= 2290)

(0.49) (0.49) (0.27) (0.29)

Treatment 0.433 0.532 0.298 0.372

(ages 7–11)

(N= 4119)

(0.50) (0.50) (0.38) (0.39)

First difference 0.026 0.116 0.006 0.039

Standard deviation shown in parenthesis below mean. No (large) increase indicates administrative posts with less

(more) than the median number of new schools built between 1993 and 1996. Standardized grade attainment is

current grade attainment as a proportion of ideal attainment given age, and ranges from 0 to 1.


in school infrastructure, B3 measures the pre-program difference in enrolment rates, and B4

is the double difference estimator of the impact of new schools (the ‘program’) on

enrolment. Two indicators of program exposure were used: the first measuring the actual

change in the number of schools in the administrative post between 1993 and 1996, and

the second a dummy variable indicating a high-intensity administrative post, defined as an

administrative post where the number of new schools built is above the median.16

Estimates of Eq. (2) are presented in Table 7b for the whole sample, and separately for

boys and girls. Columns 1–3 use the actual change in schools built between 1993 and

1996 as the measure of program exposure, and the coefficient on the relevant interaction

term shown in the last line of the table indicates effects that are larger than the cross-

section results, although the pattern of significance is the same. For example, the cross-

section estimate of the impact of an additional school for the full sample is 0.001,

compared to 0.003 in column 1 of Table 7b. The estimates in columns 4–6, which use the

Table 7a

Marginal impact of school access indicators on EP1 Enrolment

1 2 3 4 5 6

All Boys Girls All Boys Girls

Log p.c. consumption 0.058

(2.27)

0.053

(1.54)

0.061

(1.97)

0.054

(2.09)

0.048

(1.36)

0.058

(1.84)

Residuala 0.085

(2.90)

0.112

(2.79)

0.060

(1.62)

0.090

(3.01)

0.119

(2.86)

0.062

(1.60)

Head literate 0.125

(6.02)

0.116

(3.83)

0.146

(5.51)

0.122

(5.69)

0.108

(3.47)

0.146

(5.38)

Adult with EP2 0.174

(4.56)

0.164

(3.33)

0.192

(3.97)

0.175

(4.52)

0.170

(3.42)

0.188

(3.84)

Female adult with EP1 0.172

(4.15)

0.137

(2.50)

0.210

(4.29)

0.163

(3.92)

0.122

(2.24)

0.204

(4.10)

Age of child in years 0.063

(11.39)

0.076

(8.69)

0.051

(6.57)

0.063

(11.28)

0.074

(8.40)

0.053

(6.67)

Log (travel time

to nearest school)

�0.055

(7.77)

� 0.058

(7.12)

� 0.049

(5.88)

�0.052

(7.37)

� 0.056

(6.96)

� 0.046

(5.46)

Number of cement

classrooms in AP

0.001

(2.23)

0.001

(0.91)

0.002

(2.45)

0.002

(2.95)

0.001

(2.10)

0.002

(2.54)

Number of

schools in AP

0.001

(1.53)

0.002

(2.52)

0.000

(0.27)

Change in number of

schools 1993–1996

0.001

(2.04)

0.002

(1.77)

0.001

(1.64)

Observations 4290 2097 2193 4119 2010 2109

Log likelihood � 2527 � 1229 � 1262 � 2453 � 1189 � 1231

Numbers shown are marginal probabilities derived from probit estimation, with absolute z-statistics in

parenthesis. School quality variables are measured at administrative post level, except for distance to nearest

school. Constant term, provincial dummy variables, land holdings, possession of agricultural equipment, and

indicator for commercial crop production not shown. Mean of dependent variable is 0.51 and 0.40 for boys and

girls, respectively, and 0.47 for the full sample.a T-statistic is test for exogeneity of log p.c. expenditure.

16 This second indicator eliminates the influence of a few administrative posts reporting unreasonably large

increases in school infrastructure.


dummy variable for high-intensity administrative posts, also show a similar pattern of

statistical significance to the cross-sectional estimates but higher marginal effects.

3.3.3. Results on school quality

Table 8 presents probit marginal probability estimates of the impact of school quality on

EP1 enrolment by gender in rural Mozambique.17 Columns 1–3 present the base estimates

with quality measured by the number of trained teachers and the average administrative

post level pupil–teacher ratio; the latter is insignificant but the former is positive and

significant, although the quantitative effect is small.18 Adding 10 more trained teachers

will raise the probability of enrolment by 1 percentage point.

When the total number of trained teachers is split into the proportion of male and

female trained teachers and entered as two variables (columns 4–6), only the proportion of

female trained teachers is significant, with the impact especially large for girls. Note that

there are very few female trained teacher in rural Mozambique (an average of 11 per

administrative post, or roughly 11% of all teachers per region). The mean of this variable is

0.08—doubling this would increase enrolment by just over 2 and 4 percentage points for

boys and girls, respectively.

Table 7b

Double difference estimate of impact of school access on EP1 Enrolment

Measure of Number of new schools built Dummy for high intensity AP

program intensity:1 2 3 4 5 6


Change in number

of schools in

AP (� 100)

� 0.076

(0.73)

� 0.163

(1.01)

0.067

(0.54)

1 if large change

in schools in AP

� 0.011

(0.31)

� 0.055

(1.22)

0.049

(0.94)

Log (travel time to

nearest school)

� 0.039

(5.07)

� 0.047

(5.36)

� 0.028

(2.73)

� 0.039

(5.03)

� 0.047

(5.33)

� 0.029

(2.77)

Log (time) * change

in schools

� 0.012

(1.70)

� 0.009

(0.95)

� 0.016

(1.51)

� 0.013

(1.82)

0.010

(1.10)

� 0.016

(1.50)

1 if treated cohort

(7–11)

0.405

(8.66)

0.397

(5.31)

0.394

(4.87)

0.406

(8.53)

0.396

(5.00)

0.395

(4.88)

Treated cohort *

change in schools

0.003

(3.68)

0.004

(2.41)

0.001

(1.23)

0.094

(2.43)

0.158

(3.38)

0.006

(0.10)

Observations 6409 3247 3162 6409 3247 3162

Log likelihood � 3901 � 2017 � 1852 � 3900 � 2014 � 1853

In columns 1–3, the program is measured by the number of new schools built in the AP between 1993 and 1996.

In columns 4–6, the program is measured by a dummy variable indicating whether the number of new schools

built in the AP during this period is above the median (5). Probit marginal probabilities are shown with associated

absolute z-statistics in parentheses. Control variables are the same as those in (a), plus interactions between

province and the measure of program intensity.

18 When the proportion of teachers who are trained is used, its coefficient is positive but not significant.

17 I do not estimate a difference-in-differences model for the impact of school quality because there was no

significant change in the mean values of the quality indicators between 1993 and 1996.


A recent participatory study sponsored by OXFAM (1999) in Mozambique reports that

male teachers often force students to perform chores for them such as fetching wood and

water, and that parents are reluctant to send girls to school to be taught by male teachers.

This is especially true in the heavily moslem provinces of Zambezia and Nampula, and

may explain the strong positive effect of trained female teachers reported in Table 8.

3.4. Adult literacy programs and children’s school enrolment

Table 7a reported strong effects of adult education, especially basic adult female

schooling, on the enrolment probabilities of children. Adult literacy, particularly female

literacy, is well known to be a strong determinant of children’s health, nutrition, and

schooling outcomes in developing countries, and this is true in rural Mozambique as well

(Ministry of Finance, 1998). The IAF community questionnaire provides information on

whether a sample village has had an adult literacy program, and if so, the year of the

program. Only 2.5% of the villages report having had such a program, and two-thirds of

these programs occurred since 1994. Although the incidence of villages with programs is

very small in the sample (and thus unlikely to yield statistically precise results), it is

interesting to get some initial idea of whether these programs can have an effect on

children’s schooling through their impact on adult literacy.

Table 9a shows the enrolment rates of children ages 7–11 (treatment) and 14–17

(control) according to whether they live in a village that ever had a literacy campaign, or

had a recent campaign (since 1994). The last line of the table indicates that the literacy rate

of household heads is higher in villages that had an adult literacy campaign relative to

Table 8

Marginal impact of school quality indicators on EP1 Enrolment


Log (travel time

to nearest school)

� 0.054

(7.74)

� 0.058

(7.02)

� 0.049

(5.88)

� 0.056

(8.10)

� 0.061

(7.40)

� 0.050

(6.15)

Pupil– teacher ratio 0.001

(1.08)

0.000

(0.35)

0.002

(1.30)

0.001

(1.15)

0.000

(0.16)

0.002

(1.70)

No. of trained

teachers in AP

0.001

(3.70)

0.001

(3.81)

0.001

(2.40)

Proportion of female

teachers who

are trained

0.356

(2.85)

0.291

(1.65)

0.473

(3.43)

Proportion of male

teachers who

are trained

� 0.033

(0.37)

� 0.095

(0.78)

� 0.007

(0.07)

Proportion of

female teachers

� 0.061

(0.24)

� 0.045

(0.13)

� 0.172

(0.63)

Observations 4290 2097 2193 4290 2097 2193

Log likelihood � 2528 � 1228 � 1265 � 2530 � 1232 � 1260


parenthesis. School quality variables are measured at administrative post level, except for travel time to nearest

school, which is for the village. The regressions also include all the control variables indicated in Table 7a. Mean

of dependent variable is 0.51 and 0.40 for boys and girls, respectively, and 0.47 for the full sample.


those that did not, and enrolment of children 7–11 is similarly higher (by about 15–18

percentage points—see row 2) in these same villages. However, the first row of Table 9a

indicates that villages with recent literacy programs had pre-program enrolment rates that

were also significantly higher (by 8 percentage points: 0.40 versus 0.48) than those with

no program. The difference-in-differences in school enrolment rates is 7 percentage points,

which is less than half the simple cross-sectional difference, but is still large.

Table 9b replicates the regression estimates in Table 7b for school access, but now

includes an indicator of whether a village had an adult literacy campaign (column 1) or a

recent campaign (column 2). Despite the very small variation in this variable, column 1

reports a large positive and significant relationship between the presence of a campaign

and children’s school enrolment probabilities. Column 2 measures only recent campaigns,

the mean of which is only about 1.6% in the sample, and the resulting coefficient estimate

is not statistically significant, although the point estimate (0.11) is of the same magnitude

as that for head’s literacy (0.12), which continues to be statistically significant.

Table 9a

Village adult literacy programs and cohort enrolment rates

No program Had program Had recent program

Control (14–17) 0.40 (0.49) 0.52 (0.50) 0.48 (0.51)

Treatment (7–11) 0.46 (0.50) 0.64 (0.48) 0.61 (0.49)

First difference 0.06 0.12 0.13

Literacy of household heads 0.44 0.52 0.62

Recent program is one that occurred after 1993. Standard deviation in parenthesis beside proportion.

Table 9b

Marginal impact of village adult literacy campaigns on EP1 Enrolment

(1) (2)

dP/dX z dP/dX z

Log p.c. consumption 0.056 2.22 0.058 2.23

Residuala 0.084 2.91 0.080 2.72

Head literate 0.123 5.99 0.124 5.86

Adult with EP2 0.178 4.66 0.178 4.64

Female adult with EP1 0.174 4.15 0.171 4.08

Age of child in years 0.065 11.72 0.065 11.80

Log (travel time to

nearest school)

� 0.053 7.77 � 0.055 8.05

Number of cement

classrooms in AP

0.001 2.21 0.001 2.10

Number of schools in AP 0.001 1.59 0.001 1.61

Village had adult

literacy program

0.200 2.59

Village had ‘recent’

adult literacy campaign

0.114 1.17

Observations 4273 4247

Log likelihood � 2506 �2491


parenthesis. See notes to Table 7a for explanation of variables. Recent literacy campaign means after 1993.a T-statistic is test for exogeneity of log p.c. expenditure.


Table 9c presents difference-in-differences estimates of the impact of literacy cam-

paigns on school enrolment rates, based on Eq. (2) and using the two measures (ever had a

program, and had a recent program). The results in column 2 are theoretically more valid

because the control group did not have exposure to the program during their first few years

of eligibility for primary school. In column 2, the difference-in-differences point estimate

is 0.135, which is slightly higher than the cross-section estimate in Table 9b, but is not

statistically significant, probably because of the very small variation of this variable in the

sample.

3.5. Interactions with household characteristics

The influence of community infrastructure (such as school quality) may be different in

households with different characteristics. For example, the impact of a village school

may be greater for richer households if richer households are better able to take

advantage of the school. On the other hand, richer households may be able to afford to

send children to a neighboring village for schooling, in which case the impact of

constructing a school in the village may actually be greater among poorer households,

who otherwise would not have sent their children to study. The impact of community

infrastructure on household behavior may also depend on the education of adults or

parents, due to differences in preferences or access to information. In the child health

Table 9c

Double difference estimate of impact of literacy program on school enrolment

(1) (2)b

Village had literacy program 0.088 (1.01) � 0.024 (0.30)

Treated cohort (7–11) 0.409 (8.87) 0.409 (8.92)

Treated cohort * literacy program 0.098 (1.15) 0.135 (1.11)

Observations 6654 6609

Log likelihood � 4007 � 3977

Numbers are marginal probabilities derived from probit coefficients, with absolute z statistics in parenthesis.b Recent literacy programs only (those after 1993).

Table 10

Estimation results for school access indicators interacted with head’s literacy and household income

Interactions with: Interactions with head’s literacy Interactions with household income

All (1) Boys (2) Girls (3) All (4) Boys (5) Girls (6)

Log (travel time to

nearest school)

0.011

(1.04)

� 0.015

(1.10)

0.008

(0.60)

0.029

(3.01)

0.029

(2.12)

0.027

(2.69)

No. of cement

classrooms in AP

0.001

(1.36)

0.002

(1.54)

0.001

(0.45)

0.001

(1.01)

� 0.001

(0.93)

0.026

(2.31)

No. of EP1

schools in APs

� 0.001

(1.07)

� 0.000

(0.18)

� 0.001

(0.95)

0.001

(0.99)

0.002

(1.10)

0.001

(0.45)

Numbers shown are marginal probabilities derived from probit coefficients of the interaction of each variable with

literacy of household head (columns 1–3) and household consumption (columns 4–6). Absolute z-statistics are in

parenthesis. Travel time is measured at the village level. Number of observations, mean of dependent variable,

and other control variables are the same as in Table 7a.


Table 11

Estimation results of school quality indicators interacted with head’s literacy and household income

Interactions with: Interactions with head’s literacy Interactions with household income

1 2 3 4 5 6 7 8 9 10 11 12

All Boys Girls All Boys Girls All Boys Girls All Boys Girls

Log (travel time

to nearest

school)

0.012

(1.08)

0.016

(1.13)

0.008

(0.58)

0.010

(0.89)

0.013

(0.95)

0.006

(0.44)

0.026

(2.77)

0.025

(1.91)

0.025

(2.46)

0.027

(2.78)

0.027

(2.04)

0.023

(2.26)

Pupil– teacher

ratio

� 0.000

(0.17)

0.000

(0.06)

� 0.000

(0.07)

0.001

(1.18)

0.001

(0.76)

0.002

(1.27)

No. of trained

teachers

� 0.000

(0.63)

0.000

(0.51)

� 0.000

(1.10)

0.000

(0.42)

� 0.000

(1.78)

0.001

(1.90)

Proportion of

female teachers

who are trained

0.298

(1.36)

0.462

(1.55)

0.138

(0.53)

0.057

(0.36)

� 0.025

(0.10)

0.159

(0.82)

Proportion of

male teachers

who are trained

� 0.071

(0.51)

� 0.050

(0.26)

� 0.121

(0.70)

0.060

(0.41)

� 0.002

(0.01)

0.077

(0.48)

Proportion of

female teachers

� 0.513

(2.06)

� 0.415

(1.02)

� 0.540

(1.72)

0.158

(0.70)

0.096

(0.30)

0.208

(0.75)

Numbers shown are marginal probabilities derived from probit coefficients of the interaction of each variable with literacy of household head (columns 1–2) and

household consumption (columns 3–4). Absolute z-statistics are in parenthesis. School quality variables are measured at administrative post level, except for travel time,

which is measured at the village level. Number of observations, mean of dependent variable, and other control variables are the same as in Table 8.

S.Handa/JournalofDevelo

pmentEconomics

69(2002)103–128

120

literature, for example, the impact of mother’s education has been found to vary

significantly with community characteristics such as sewerage and sanitation conditions

(Thomas et al., 1991; Barrera, 1990).

Both household income (measured by expenditures per capita) and adult education

significantly influence schooling choices in Mozambique, and school infrastructure also

conditions these choices in rural areas. Does the impact of school infrastructure depend on

household characteristics? Are certain households more likely than others to change their

schooling decisions in response to variations in school infrastructure? These questions are

addressed by interacting the different school supply characteristics with household adult

education (measured by the literacy of the head) and household income, to see if

significant interactions indeed exist between school supply and household characteristics.

The interactions are tested sequentially, first by interacting the school supply variables

with head’s literacy, and then by interacting the same variables with household (log) per

capita consumption. Results are presented separately for two dimensions of school supply

(access and quality) in Tables 10 and 11.

Starting with school access, Table 10 presents the results of the interactions between

each school access indicator and head’s literacy (columns 1–3) and household

consumption (columns 4–6). Significant interactions exist among several access indi-

cators and household income (columns 4–6). For both boys and girls, distance to a

primary school and household income are substitutes, hence, the positive impact of

constructing a school nearby will be greater among poorer households. Furthermore, the

positive impact of cement classrooms on girls’ enrolment is enhanced among richer

households, given by the positive and significant coefficient on the interaction term in

column 6.

Table 10 presents the results of the estimates of school quality indicators interacted

with head’s literacy (columns 1–6) and household consumption (columns 7–12). For

the full sample, the impact of the proportion of female teachers depends on whether the

head is literate or not. The negative coefficient on the interaction term (column 4)

implies that these two characteristics are substitutes, and therefore, the impact of these

dimensions of school quality is significantly greater among households where the head

is not literate. The results for income in columns 7–12 show one marginally significant

coefficient. For girls, the impact of the number of trained teachers in the AP varies with

household income; the positive coefficient in this case implying complementarity

(column 9).

4. Policy simulations

According to the Ministry of Education’s strategic plan, raising basic primary education

levels is a priority for Mozambique. In this section, the relative impact of demand side

versus supply side interventions on primary school enrolment rates in rural Mozambique is

compared. The simulations are based on the probit regressions for the determinants of

current enrolment of children aged 7–11 years old in rural areas. The school character-

istics included in the model are the number of trained teachers and the pupil– teacher ratio,

and the number of schools and cement rooms in the administrative post. All the household


level characteristics mentioned above are included in the model, as well as the village level

variable indicating the travel time to the nearest school. Because of Mozambique’s vast

size and geographical and economic heterogeneity, the impact of the hypothetical policy

interventions to vary by province by interacting the policy variables with provincial

dummy variables was allowed.19 Systematic differences in the effect of policy interven-

tions on boys’ and girls’ enrolment rates were not found and so estimates for the full

sample only are provided.

4.1. Supply side simulations

The supply side policy simulations consider the impact on enrolment rates of

increasing the number of schools in rural areas in Mozambique. The IAF community

questionnaire indicates that approximately 68% of rural villages have a basic primary

school, and the regression analysis shows that the distance to a school is an extremely

important determinant of children’s enrolment. The increase in EP1 enrolment that would

occur due to two separate interventions was calculated: (1) increasing the overall EP1

coverage rate to 79%, which implies building a school in 70 villages per province; (2)

increasing the overall EP1 coverage rate to 89%, which implies building schools in 140

villages per province. These rates are attained by increasing the number of schools in

each administrative post. In order to capture the impact of school characteristics (and not

just access) on enrolment, it was assumed that each school consists of three cement

rooms and comes with two trained teachers. The addition of a school in an administrative

post will reduce the average travel time to the nearest school. This indirect effect due to

changes at the administrative post level is accounted for in the policy simulations by

reducing the travel time for villages in an administrative post that receives an additional

school.20

4.2. Demand side simulations

The impact of policy interventions designed to influence demand side (or household)

characteristics is based on the same model used for the supply side simulations. Two

types of interventions are simulated, one influencing household income (or consump-

tion) and the other influencing adult education. The income-related interventions involve

raising the per capita consumption of all households to at least the level of consumption

of the 25th percentile of the per capita consumption distribution (Mt. 2494 per person

per day in the IAF, or approximately 25 US cents); the second policy is to raise all

households to at least Mt. 3584, which is equal to median consumption in the IAF. Since

these interventions only affect poor households, they will not be evenly distributed

throughout the country. In particular, the poorer the province, the larger the share of

20 The average reduction in travel time was calculated by estimating an OLS regression for the relationship

between number of schools in an administrative post and village travel time. The estimated coefficient on the

variable ‘number of schools in the AP’ is used to adjust the travel time variable in the simulation.

19 Detailed simulation results by province are not presented here, but are discussed in Handa (2000).


households in the bottom 25th percentile or bottom half of the per capita consumption

distribution, and thus, the larger the number of households who will be affected by the

policy. The second demand side simulation is motivated by the results presented above,

indicating that adult household education significantly conditions children’s schooling.

The impact on enrolment rates was simulated if all household heads in the bottom

quartile of the per capita expenditure distribution were literate. As in the income case,

the benefits of this intervention will not be distributed equally across provinces. While

poorer provinces have more eligible households, the policy only affects heads of

households who are not literate and so the proportion of heads who are literate also

matters.

4.3. Results of simulations

Results of the supply and demand side simulations are presented in column 1 of Table

12. These are calculated as the percentage change in overall mean enrolment with respect

to the baseline figure for predicted enrolment derived from the probit estimates without

any simulations. The policy of increasing EP1 coverage to 79% (row 1) will increase

overall enrolment by 13%, while doubling the size of the intervention would raise

enrolment by 35%. Rows 3 and 4 provide estimates of the percentage change in enrolment

due to the two income-related policy interventions described above. The overall (national)

impact is to raise enrolment rates by 2% and 4%, respectively—these effects are

significantly smaller than the estimated enrolment effects of building more schools. The

last two rows of column 1 in Table 12 present simulation results based on interventions

that raise the literacy level of heads of households in the bottom parts of the per capita

consumption distribution. The overall impact of this intervention is substantially larger

than the income intervention. Increasing literacy of heads in the bottom quartile would

increase overall enrolment by 8%; increasing literacy of heads in the bottom half of the

distribution would increase enrolment rates by 15%.

Note that if it is not literacy itself, but factors associated with literacy that lead to

increased child schooling (such as preferences or value for education), then the simulation

Table 12

Policy simulations and cost-effectiveness of supply and demand side interventions

Intervention (1) (2) (3) (4)

Benefita Unit cost

(US$)

Total cost

(US$ million)

Effectiveness:

(3)/(1)

(US$ million)

Build 70 schools per province 13 70,000 49 3.8

Build 140 schools per province 35 70,000 98 2.8

Bring Households to 25th percentile 2 29b 24 12.0

Bring Households to 50th percentile 4 55b 91 22.8

Literacy to heads in bottom quartile 8 30 14.7 1.8

Literacy to heads in bottom 2 quartiles 15 30 27.9 1.9

a Percentage increase in enrolment based on simulation results. See text for details.b Average per household for 1 year.


results are likely to overestimate the benefits of adult literacy campaigns in Mozambique. I

have tried to control somewhat for these family-specific tastes or talents for education by

including the education level of other adult household members in the regression

equations. These other variables actually reduce the estimated impact of head’s literacy

by 25%.21 Moreover, one may argue that family-specific preferences for education are

likely to be smaller in a supply-constrained society such as rural Mozambique, relative to

more developed countries like the United States or Canada, where the spatial supply of

schools is relatively abundant.

4.4. Cost effectiveness of policy options

The simulations presented above provide an idea of the overall benefit of different

policy interventions without considering the cost of these same interventions. I have

gathered approximate costs for adult literacy campaigns and school construction from

NGOs working in the education sector in rural Mozambique. The cost of building a basic

three-room cement school in rural Mozambique is estimated to be US$50,000. To this

construction cost, the cost (including administrative costs) of two teachers for 10 years

was included, which, according to the pay structure for teachers, adds an additional

US$20,000 to the cost of a rural primary school. The policy simulation in row 1 of Table

12 calls for building 70 schools in each of the 10 rural provinces, at a cost of US$70,000

per school, or a total cost of US$49 million. Dividing this figure by the percentage

increase in enrolment (13%) gives approximately US$3.8 million per percentage point

increase in enrolment.

Kulima, a local NGO that has provided adult literacy campaigns in rural Mozambi-

que, estimates a total cost per adult of US$30 for the delivery of a 1-year literacy

program in a rural village. According to the IAF, and using population weights, there are

approximately 490,000 illiterate heads of household in the bottom quartile (59% of

heads in the bottom quartile cannot read or write), and approximately 930,000 illiterate

heads in the bottom two quartiles (54% of heads are illiterate among this group).

Providing literacy for the heads in the bottom quartile at a cost of US$30 per person

leads to a total cost of US$14.7 million, which when divided by the expected percentage

increase in enrolment (8) yields US$1.8 million per percentage point increase in

enrolment.

Finally, using the (population weighted) figures for per capita household consumption

in the IAF, the total amount of transfer required to bring all households below the 25th

percentile to a per capita household consumption exactly equal to consumption in the 25th

percentile was calculated. This figure is US$24 million per year, and when divided by the

expected percentage increase in enrolment (2%–see column 1 of Table 12), yields US$12

million per unit of expected benefit.

The approximate costs associated with each intervention and the associated cost-

effectiveness numbers are shown in columns 2, 3, and 4 in Table 12 for each policy

21 When the variables ‘number of adults with EP2’ and ‘number of adult females with EP1’ are excluded

from the regressions, the estimated impact of ‘head literate’ increases by 25% for both boys and girls.


simulation. These estimates clearly show that the income intervention is the least cost-

effective method of raising primary school enrolment in rural Mozambique. The policies

of adult literacy and improved access to schools are significantly more cost-effective

methods of raising enrolment rates, with adult literacy providing a slightly cheaper

alternative among these two options.

5. Conclusions

Raising primary school enrolment is a major development imperative, although the

interventions that can best raise enrolment are not always straightforward, and can vary

both between and within countries. Using the first national household survey of

Mozambique, coupled with detailed information on school infrastructure supplied by

the Ministry of Education, this paper evaluates the relative importance of supply and

demand side factors in determining rural primary school enrolment. Simulations based

on a set of ‘plausible’ demand and supply side interventions indicate that in rural

Mozambique, building more schools or raising adult literacy will have a larger impact

on enrolment rates than interventions that raise household income. For example, raising

the EP1 coverage rate to 79% in rural areas will increase enrolment rates by 13%, while

making household heads literate in the bottom per capita consumption quartile will raise

rural primary school enrolment by 8%. In contrast, bringing per capita consumption of

the poorest quartile up to Mt. 2494 per day will raise rural enrolment by a mere 2%.

When relative costs are considered, adult literacy campaigns become more attractive,

with cost-effectiveness ratios that are 6–10 times better than the income intervention,

and 1.5–2.5 times better than building more schools. Even if we assume that half the

measured benefit of adult literacy is through unmeasured tastes for, or ability in,

acquiring human capital, adult literacy is just as cost-effective as extending coverage

through school infrastructure.

The detailed analysis of the impact of school characteristics on primary school

enrolment in rural Mozambique indicates that dimensions of school quality and access

both work to stimulate enrolment, although the effects are small and differ somewhat by

gender of child. School quality, measured by the number of trained teachers in the

administrative post, has a positive and significant impact on enrolment, but it is the

gender composition of the trained teaching staff that is even more important in

determining the household decision to send children to school. For example, the share

of female teachers who are trained is an important positive determinant of enrolment

rates. Raising this ratio from 0.08 to 0.16 in the administrative post will raise enrolment

rates by roughly 4 percentage points.

School availability also has a significant impact on enrolment rates. Reducing the travel

time to the nearest school will increase enrolment rates for both sexes by 17–20

percentage points, and the impact of school availability is enhanced for girls if the school

is built with cement.

Few previous studies have considered the possible interaction between school supply

indicators and household characteristics. In Mozambique, these exist particularly for girls.

In terms of policy, the most interesting of these is the positive interaction between travel


time to a school and household income, which implies that the two factors are

substitutes—construction of a village school will increase enrolment more among poorer

households. Additionally for girls, there is a positive interaction among household income,

cement classrooms, and the number of trained teachers in the area.

Acknowledgements

Thanks to Farizana Omar, Helder Zavale, and Virgolinho Nhate for excellent

research assistance, to Manuel Rego of the Mozambique Ministry of Education for

supplying and interpreting the data, and Gaurav Datt, Dean Jolliffe, and especially Ken

Simler, for helpful comments on earlier drafts. Useful criticism was also provided by

an anonymous referee. This paper was written while the author was outposted by

IFPRI as Professor of Economics to the Eduardo Mondlane University, Maputo,

Mozambique.

Appendix A1. Summary statistics for children ages 7–11

Girls Boys

Mean SD Mean SD

Log daily per capita consumption 8.154 0.61 8.143 0.64

Land holdings (ha) 2.564 2.41 2.590 2.70

Have irrigation 0.046 0.21 0.046 0.21

Have agricultural equipment 0.046 0.21 0.042 0.20

Head literate 0.476 0.50 0.454 0.50

Adult in household with EP2 0.078 0.27 0.077 0.27

Adult female in household with EP2 0.109 0.31 0.109 0.31

Head female 0.187 0.39 0.184 0.39

Head’s age 45.508 12.80 45.146 12.71

Currently enrolled in school 0.406 0.49 0.498 0.50

School characteristics

Pupil– teacher ratio 65.324 18.07 65.157 17.59

No. of trained teachers 79.654 66.47 80.414 66.90

Proportion of trained female teachers 0.096 0.10 0.094 0.11

Proportion of female teachers 0.103 0.12 0.102 0.13

Pass rate 0.644 0.05 0.643 0.05

Girls’ pass rate 0.581 0.07 0.580 0.07

Boys’ pass rate 0.676 0.05 0.676 0.05

Portuguese subject pass rate 0.663 0.05 0.661 0.05

Mathematics subject pass rate 0.683 0.05 0.682 0.05

Log (travel time to nearest school) 1.384 2.11 1.317 2.10

Number of EP1 schools in administrative post 26.118 21.08 25.945 19.78

Change in number of EP1 schools 8.383 11.35 8.301 11.77

Have EP2 in AP 0.651 0.48 0.657 0.47

Have secondary school in AP 0.272 0.44 0.273 0.45

Number of observations 2293 2203

School characteristics are measured at the administrative post level, except for travel time to school.


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