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ARTICLE IN PRESS International Journal of Educational Development 28 (2008) 419–434 www.elsevier.com/locate/ijedudev Back to school in Afghanistan: Determinants of school enrollment Stephane Guimberta, Keiko Miwaa,Ã, Duc Thanh Nguyenb a The World Bank, 1818 H Street NW, Washington, DC 20433, USA Faculty of Economics, National Economics University, Ha Noi, Vietnam b Abstract One of the first achievements of post-conflict Afghanistan was to bring almost million children back to school Issues remain daunting, however, with low primary enrollment especially for girls and in rural areas and very weak learning achievements We review some key features of the education system in Afghanistan By matching household and school data, we assess the impact of various factors on enrollment Overall, the analysis indicates that further increasing supply alone is unlikely to lead to higher enrollment The analysis confirms the importance of demand factors such as the education of parents, the family language, and other community and ethnic factors r 2007 Elsevier Ltd All rights reserved Keywords: International education; Development; Education policy; Post-conflict; Demand and supply of education Introduction Promoting education ranks very high among the stated top priorities of the new Afghan Parliament Members (Wilder, 2005) This is understandable given the critical needs of a country where almost 80% of the population is illiterate Education is central not only to the growth and poverty reduction agenda, but also to the empowerment, democratization, and governance agendas In fact, primary and secondary education (9 years) is a constitutional requirement (Article 17) At the same time, attacks and death threats on schools and teachers are becoming frequent, turning ‘‘playgrounds into battlegrounds’’ (The Guardian, 2006) This highlights, in a ÃCorresponding author Tel.: +1 202 473 1000; fax: +1 202 522 2102 E-mail address: kmiwa@worldbank.org (K Miwa) 0738-0593/$ - see front matter r 2007 Elsevier Ltd All rights reserved doi:10.1016/j.ijedudev.2007.11.004 chilling manner, how symbolic progress in the area of education will be for the future of Afghanistan This paper reviews progress toward universal primary enrollment, key obstacles, and success factors Afghanistan in the middle of the 20th century was a poor, largely uneducated country (with less than 5% of the children in school, Fig 1, cf also World Bank, 2005) Secular education had been introduced in Afghanistan at the end of the 19th century (Nancy Dupree, in World Bank, 1999) and, except for a conservative break in 1929, some progress was made throughout the century A more significant effort was made in the 1960s and 1970s By 1980, more than 30% of children went to school, including around 20% of girls But the long period of conflict took a heavy toll on Afghanistan, with (after the bloodless 1973 coup that overthrew the monarchy) a communist coup (Sawr Revolution) in April 1978, the Soviet invasion in December 1979, ARTICLE IN PRESS S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 420 5,000,000 Male 4,500,000 100 90 Female 4,000,000 80 70 3,500,000 3,000,000 Typical transition 60 50 40 2,500,000 2,000,000 1,500,000 30 20 1,000,000 500,000 1940 1955 1970 1985 1999 2004 Afghanistan 10 1940 1960 1980 2000 2020 2040 2060 Fig Primary and secondary enrollment: (a) enrollment in Afghanistan (] children); (b) enrollment rate in Afghanistan compared to the ‘‘typical transition’’ observe in other countries (%) Note: the ‘‘typical transition’’ is based on experience in other countries over 1960–2000, fitting an ‘‘S-shaped’’ curve over the timing of improvements in net primary enrollment in other countries (Clemens, 2004) Source: MoE, UNESCO, and UNICEF for various years; Clemens (2004) the withdrawal of Soviet troops in February 1989, the collapse of the Najibullah Government in April 1992, the Taliban entry in Kabul in 1996, and the military actions that followed the events of September 11, 2001 Total enrollment decreased over that period, but was sustained at a minimum level through intensive work by non-governmental organizations (NGOs) Girls’ enrollment continued to increase slightly in the 1980s, but dramatically fell in the 1990s Despite impressive progress, challenges remain many Progress over the last years has been very positive, with enrollment increasing from to million Given the large young population (according to GoA, 2006, there are more than 10 million children, the highest proportion of youth in the world), this represents a net enrollment ratio of more than 50% Similarly, in 2001 very few girls were going to formal schools and more than a million are now enrolled However, as discussed in Clemens (2004), the worldwide experience of universal education is that it takes time: with a traditional pattern of moving toward this goal, Afghanistan would only reach universal primary and secondary education in several decades (Fig 1b) The objective of this paper is to understand the drivers of enrollment, with a view to make recommendations on ways to achieve further progress toward universal enrollment and completion At the same time, we present a brief overview of the education system in Afghanistan We also consider the quality of education: despite very limited data on this issue, it is widely viewed as equally, if not more, important to access issues in post-conflict reconstruction efforts (see Buckland, 2005, for instance) There is a substantial body of research on this topic across countries Glewwe and Kremer (2005) provide a good summary In Afghanistan, the only econometric analysis is Rashid (2005), which is based on household survey data in rural areas (2003 NRVA) and is limited to the demand side Our paper expands this research by using data on schools and public expenditures to analyze the supply side as well the demand side This will complement research carried by Hunte, which also addresses the issue of decision making about enrollment, but uses a qualitative approach based on in-depth interviews (Hunte, 2005, 2006) Given the poor quality of data available to us and the nature of this research, exploiting such complementarities between qualitative and quantitative analysis is critical The paper is structured as follows The next section reviews the analytical framework and the datasets we use in this paper The following section presents key summary statistics on education, both on the supply and demand side The fourth section presents our empirical estimates of enrollment correlates in rural areas The final section concludes with a summary of findings, recommendations, and suggestions for future analysis Empirical strategy When asked why their children were not going to school, households raise a number of reasons that ARTICLE IN PRESS S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 421 Table Reasons for non-enrollment (%) Supply-side constraints Demand-side constraints Reasons National Major cities Other urban All urban All rural Too far Inadequate facility No separate school Teachers gender Inadequate sanitation Domestic work Not necessary Household income Expensive Feel ashamed Others 37.2 25.8 22.0 6.4 1.0 17.2 15.0 7.1 5.2 4.4 21.1 20.4 0.3 1.2 0.3 2.4 18.4 27.3 13.1 12.0 5.3 22.7 29.9 17.6 24.0 3.1 1.4 18.8 14.0 7.6 4.3 3.2 17.6 25.5 9.6 13.6 1.8 1.9 18.7 20.1 10.1 7.8 4.2 19.9 39.5 29.0 23.6 7.3 0.9 16.9 14.1 6.6 4.7 4.5 21.3 Note: multiple answers possible Source: GoA and UNICEF (2003) Source: World Bank (2006) can be arranged into two groups (Government of Afghanistan (Central Statistics Office) and UNICEF, 2003, and Table 1): supply-side (lack of school, or lack of girl school or female teacher) and demand-side (opportunity cost to send children to school, traditions) Lack of education provision and absence of girl school and female teacher are notably important factors in rural areas, while costs are more an issue in urban areas (which can be related either to the direct cost of schooling or to the opportunity cost of sending a child to school).1 As discussed in WFP et al (2004), there are a number of issues in interpreting these answers We therefore seek to assess whether available data corroborate these answers In this section, we present our empirical strategy and the datasets we use in this paper characteristics As noted in Glewwe and Kremer (2005), this is a reduced form, with at least one key driver (the school inputs under the parent’s control, such as daily attendance, purchase of textbooks, etc.) absent from the specification We note four key econometric issues with our analysis (see Glewwe and Kremer): 2.1 Framework A standard approach consists in comparing the benefits and costs of attending school (see for instance Bedi and Marshall, 2002) Such approach can usually be summarized (Glewwe and Kremer, 2005) as follows: E ¼ f ðS; C; H; RÞ, where E is enrollment (it could be a measure of learning achievement such as a result to a test), S is a vector of school and teacher characteristics, including cost and quality, C is a vector of child characteristics, H is a vector of household characteristics, and R a vector of regional or community WFP et al (2004) and Hunte (2005) diagnose a similar combination of reasons We are likely to omit variables To partially address this, we run a regression using some subjective data on ‘‘what is the main reason for not going to school’’ to better understand some of these omitted variables such as ‘‘tradition’’ Yet many child characteristics are unobservable, while, as discussed in Hunte (2005), they can be important drivers of the decision to go to school, such as parents declaring that their child is ‘‘too lazy’’! It is almost equally certain that our variables are subject to a measurement error, which weakens the quality of the regressions and underestimates the effects A particular concern is the large number of non-response on the enrollment question (see below) Omitted variables, notably issues related to school quality and ethnic issues, are potentially correlated with the error term, in the sense that Government could be more likely to build schools and post-teachers in places with high intrinsic demand and established quality schooling This phenomenon also might well be present for teaching materials, which are by and large provided outside Government channels by donors and NGOs (even for schools run by the Government) This creates a so-called ‘‘endogenous program placement’’ bias We will try to ARTICLE IN PRESS S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 422 reduce this bias by including regional dummies as this phenomenon is likely to be heavily region specific There may be a selection bias if parents can choose schools We assume that this is not the case given that our work focuses on rural areas The problem is also less likely to be major for primary education We also take some comfort from our finding that children who have recently migrated within Afghanistan have, if anything, a lower chance of being enrolled (hence it is unlikely that they have migrated just to move closer to a better school) A final note is that we use probit, logit, and linear probability model (LPM) specifications Probit models are our standard specification, while logit and LPM are used as alternatives—and we found consistent and similar results We also use robust standard errors as data are likely to present heteroskedasticity (as often with cross-sectional data) 2.2 Data We use three datasets2: Public Expenditure Tracking Survey (PETS): As part of a Public Finance Management Review (World Bank, 2006),3 a small-scale PETS was conducted in education The study involved interviews with 217 teachers and 109 head teachers spread across 109 schools The schools were chosen based on a random sampling framework from among 36 districts in nine Provinces.4 The study sought to examine current and historical budget data starting in 2002; however, due to the unavailability of past data in most provinces, this survey largely focused on 2004/05 data School Census (SC): The Ministry of Education (MoE) carried out a census of all Government We also make reference to the Multi Indicator Cluster Survey (MICS), a household survey covering 20,806 households conducted in 2003 and covering a number of health and education topics (Government of Afghanistan (Central Statistics Office) and UNICEF, 2003) This survey was designed to be representative at the provincial level This review was carried out by the World Bank in collaboration with the Asian Development Bank, the European Commission, UK Department for International Development (DfID), and the International Monetary Fund Kabul, Herat, Konduz, Ghazni, Kandahar, Sari Pol, Paktiya, Nemroz and Panjsher school facilities in Afghanistan Data were collected in 2004 and include detailed information on schools, teachers, non-teaching staff as well as some information on students National Risk and Vulnerability Assessment (NRVA): the 2003 NRVA survey was carried out by the Ministry of Rural Rehabilitation and Development in association with the Ministry of Agriculture and Animal Husbandry and with support from the World Food Program, the Food and Agriculture Organization, and the World Bank It covered all 32 provinces which existed at the time and districts within provinces, excluding 11 districts for security reasons Household-level data were collected from 85,577 individuals (43,377 female and 42,200 male members from 11,200 households) in 1850 villages Most of our analysis is based on a database that combines the last two databases, by assigning district-level data from the SC to individual data in the NRVA It should be noted that the SC was done in 2004 (1 year later than the NRVA), hence opening up some time discrepancy.5 In addition, we use the PETS database for some of our assessments of descriptive statistics and for simple regressions analysis in the next section For all three databases, a number of caveats are in order First, in the absence of a census, a household survey cannot be considered nationally representative In the case of the NRVA, the sample size somewhat makes up for this issue For the PETS, the sampling is much better (a stratified sampling based on the SC), but the size of the sample is very small, thus limiting the possibility of segmenting it to refine the analysis Second, in all three cases, security concerns imposed some restrictions on data collection Finally, all three surveys were subject to difficulties in terms of training the enumerators, cleaning up the data, and ensuring comparability of data School system and drivers of enrollment: descriptive analysis In Afghanistan, primary education consists of Grades 1–6 starting at age 6, lower secondary It also added a complexity as two new provinces were created, which we had to combine ARTICLE IN PRESS S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 not surprisingly, in most cases, children are late in the curriculum, i.e above the normal age level for their grade In addition, significant disparities, by province and by gender, exist in enrollment (Fig 3) While universal primary education enrollment is almost achieved in the three main cities of Kabul, Herat, and Mazaar, enrollment remains marginal (below 20%) in the south of Afghanistan, namely, provinces of Uruzgan, Helmand, and Badghes education consists of Grades 7–9, and higher secondary for Grades 10–12 Education is provided for free at public institutions from Grade until the undergraduate level Enrollment is heavily skewed toward the lower grades and toward boys, reflecting the disruptions of the 1990s (Fig 2) The dynamics of enrollment will also imply an increase in secondary education in the coming 2–3 years (World Bank, 2006) Enrollment by age shows that, 14 13 12 11 10 Girls 700 423 600 500 400 300 200 100 - Boys - 100 200 300 400 500 600 700 Fig Enrollment by grade and gender in 2004 (‘000 students) Source: School Census (GoA) 100 90 Boys Girls Average 80 70 60 50 40 30 20 10 KABUL CITY HERAT CITY MAZAR CITY KABUL TOTAL HERAT TOTAL BADAKHSHAN KUNDUZ CITY JALALABAD CITY BALKH TOTAL BAGLAN BALKH RURAL KAPISA HERAT RURAL KONAR LAGMAN SAMANGAN PARWAN NANGAHAR TOTAL TAKHAR WARDAK FARYAB KANDAHAR CITY KABUL RURAL JAWZJAN PAKTIYA NANGAHAR RURAL LOGAR KUNDUZ TOTAL BAMYAN SAR I POL GHOR KHOST GHAZNI NIMROZ KANDAHAR TOTAL PAKTIKA KUNDUZ RURAL FARAH NOORISTAN ZABUL KANDAHAR RURAL BADGHES HELMAND ORUZGAN - Fig Disparities in enrollment (% children 7–13 years) ARTICLE IN PRESS 424 S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 Table Teaching practices Age (years) Sex (1 ¼ man) Years as teacher Years as teacher squared Years in this school if Teach shifts Grade taught Constant Number of obs Pseudo R2 Mean Who teaches double shifts What grade are they teaching 36.53 0.72 8.26 0.014 À0.021 0.022* 4.53 0.14 5.18 À0.062** À0.032* À0.125* 0.5*** À0.012*** À0.001 0.123*** À2.25*** 4.3*** 213 0.1374 213 0.168 Note: Probit regression on the dummy ‘‘teaches two shifts’’ and robust standard error OLS on ‘‘highest grade taught’’ (***), (**), and (*) mean significance at 1%, 5% and 10%, respectively Source: Authors’ calculations, based on PETS data There are also striking gender disparities; net primary enrollment is only 40% for girls and 67% for boys (Government of Afghanistan (Central Statistics Office) and UNICEF, 2003) 3.1 The supply of education The Government’s vision of the education sector, set forth in a number of policy documents, is to provide good quality education for all regardless of gender, ethnicity, language, religion and geographical location, and to provide opportunities for secondary and higher education at international standard to build skilled human resources which are able to meet private-sector-driven national development and reconstruction objectives At the moment, international assistance provides the financial means to develop quality education in Afghanistan: education is one of the main budget items after security, and the sector benefits from substantial external funding as the Afghanistan Reconstruction Trust Fund (ARTF) largely finances teachers’ salaries 3.1.1 Teachers There were around 110,000 teachers, according to the 2004 SC.6 This number has increased significantly over the last years Only 21% of teachers are female, with an even lower proportion in primary school, and this proportion is as low as 12% in rural areas Teachers for higher grades seem to be more experienced, though not necessarily older (Table 2) Surprisingly, controlling for other factors, women seem to have a better chance to teach higher grades The higher share of women in teacher-training schools is an encouraging sign for the future In all, 53% of primary teachers and 27% of secondary teachers are contract staff.7 More than nine out of 10 primary school teachers have completed only primary education (Table 3) The situation is slightly better in middle schools, notably for permanent teachers But, in total, less than 50 teachers of primary and middle schools (less than 0.1%) have higher (university) education The student-per-teacher ratio is around 39:1, which is in line with prescriptions from the Education For All (EFA) framework (Bruns et al., 2003) However this hides significant disparities across the country, ranging from 28:1 to 65:1 Although there are differences among provinces, the PETS shows that most teachers are now being paid on time and in full, a sign of improvements in the public finance management which hopefully has a positive impact on teachers’ motivation: 77% of teachers are paid monthly, 67% of them are paid on time, and 69% did not report any fee paid for receiving salary payments Further progress is likely to be challenging as recruitment of teachers seems to The PETS also include some data on head teachers They are somewhat older than teachers (42 against 37 years on average, using PETS data) and have a similar proportion of women (around 20%) or ‘‘contract’’ staff are government staff hired on fixed-term contracts ‘‘Karmand’’ are permanent, tenured government staff See Evans et al (2004) ARTICLE IN PRESS S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 425 Table Teachers in primary and secondary schools Primary school Total (teachers) Total (%) Of which % women Of which % primary level Of which % middle level Of which % high level Of which % higher level Middle school (*) Permanent Contract Permanent Contract 21,469 47 15 90 23,757 53 14 93 – 19,447 73 34 55 44 7296 27 19 81 18 – Source: based on school census (GoA) Table Access to markets and facilities (rural areas) Food market In the community Less than 1/4 day 1/4–1/2 day 1/2–1 day More than day Not available Total Nearest transportation Primary school Secondary school 5.3 46.8 27.1 13.8 6.2 0.8 34.9 33.5 13.4 5.4 3.1 9.8 46.4 31.6 6.3 1.6 0.6 13.7 11.6 32.6 9.1 3.1 1.1 42.5 100.0 100.0 100.0 100.0 Source: NRVA (2003) have outpaced the headcount ceilings and budgetary authorizations available 3.1.2 Schools There are approximately 6500 government-registered schools in the country Two-thirds are primary schools Islamic schools account for a very small proportion of enrollment, which might reflect two factors: (i) a number of these schools are not registered with the Government and hence are not in these data; and (ii) in many cases, mullahs teach religious studies to children either before or after public school, or during the summer or winter break (Hunte, 2005) It is also known that a number of informal education mechanisms exist in Afghanistan (World Bank, 1999; Hunte, 2005) There are also private school as well as some NGO schools (Hunte, 2005), but their number remains small.8 An educated guess would suggest that private and NGO schools represent 3–5% of the total number of schools, probably even less in terms of the total number of students A survey by UNICEF and MoE suggested that in 2002, of all ‘‘learning spaces’’, 31% were informal (NGO; community managed; Mosque managed; home-based) In rural areas, almost a half of households (46%) have a primary school in their community (Table 4) Overall three-quarters of households have a primary school accessible within a few hours away This is better than access to food market or even transportation Nevertheless, for young children, this can represent a significant barrier if they have to travel more than 14 day twice a day On the other hand, access to secondary schools is much lower, with less than 12% of households having a school in their community and 43% stating that they have no access to a secondary school at all There is unfortunately very little data available on the physical conditions of these schools Some of them are in fact little more than a tent for the teacher and the pupils to gather in The SC has some data on reconstruction, but there is significant underreporting, with around a quarter of the schools not reporting Setting aside these measurement errors, the Census suggests that some 29% of schools are or have been under reconstruction (reconstruction includes construction in the case of schools upgraded from a tent to a building) The proportion is greater for higher levels of schools ARTICLE IN PRESS 426 S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 (55% for higher schools against 20% for primary schools) A correlation analysis at the district level (Table A1 in annex) suggests that reconstruction activities have been more prevalent in Pashtuspeaking areas, but they are not correlated with higher enrollment (see the next section on regression analysis) 3.1.3 Budget and management Education (excluding higher education) accounts for almost 20% of total operating expenditures in the core budget in Afghanistan.9 However, allocation for non-salary expenditures is low and uneven: in 2004/05, 81% of salaries were spent in provinces but only 67% of non-salary expenditures, with even larger disparities in terms of budget allocations (World Bank, 2006) Often funds not reach the schools At the provincial level, half of the provincial education departments indicated that the Mustoufiat (the provincial office of the Ministry of Finance) did not release funds, and a quarter indicated that the central MoE did not provide for an allotment (the quarterly release of funds) The second point is ironic because the central MoE does not manage to spend its allotment in full, while the provincial departments usually spend it in full and would need more In fact, provincial departments have little say in the planning process: almost half of the surveyed provincial education departments did not even prepare an annual budget for non-salary expenditures that would have indicated their needs to Kabul (the situation was much better for the planning of teachers) The fact that the Ministry and its provincial departments hardly demand inputs for the budget process and hardly allocate funds to the service delivery units is also evident at the school level According to the PETS, only half of the schools had submitted a budget for teachers and a low 6% had submitted a budget for non-salary expenditures The same happens during the year, with schools barely asking for non-salary expenditures This is not surprising given that, even with this low level of requests, the estimated time for approval ranges from to 12 months and only 33% of the requests The ‘‘core budget’’ includes all revenues and expenditures flowing through the Government’s systems (its bank accounts; using its procurement requirements; etc.) Part of this budget is financed by donors This is opposed to the ‘‘external budget’’, which is entirely controlled by external donors are approved The PETS shows that most nonsalary expenditures are covered by non-government organizations or when they are covered by the government they come in kind from the district or provincial department This seems to be the main external contribution, as most schools reported that they collected no fee (only contributions through PTA, see below).10 Most schools (94%) reported being visited for supervision, a practice to build on to improve the quality of education (PETS) These are reasonably frequent visits (weekly for 15%, monthly for 25%, and quarterly for 37%) The supervision is often administrative (36% to assess teachers’ attendance, 17% to review financial records, 11% to review students’ attendance, 8% to review the school facility) but can also be pedagogical (17% to assess teaching practice) Finally, around 75% of schools reported having a Parent Teacher Association (PTA) and a School Management Committee (SMC) Most of these bodies were reported as contributing to school activities In 9% of the schools, the SMCs were also reported as assisting in raising funds for schools 3.1.4 Quality Very little is known about the quality of education In NRVA (2003), parents were asked about the main problem at school: lack of school books and supplies was the main issue raised (38%), together with poor quality of facilities (25%), lack of teachers (15%), and poor teaching (11%) In Human Rights Research and Advocacy Consortium (2004), a rough estimate suggests that ‘‘74% of girls and 56% of boys drop out of school by the time they reach Grade 5’’ Indeed, input indicators (such as teacher training, availability of non-salary budgets, textbooks, etc.) suggest that quality is poor There are also concerns about the pedagogical approach, including excessive reliance on memorization and rote-learning, and about the outdated curriculum, including the textbooks (World Bank, 1999) 3.2 Demand side 3.2.1 Children We now turn to the demand side of our analytical framework and first start with the characteristics of 10 It should be noted, however, that these data come from a school and teacher survey, not from a parent survey, and hence there is a potential underreporting bias ARTICLE IN PRESS S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 the children themselves First, as discussed above, a key issue is that most children are already late in the curriculum for the intended age/grade Second, disability is a constraint that affects 2.5–3.0% of 7–13-year-old children (Government of Afghanistan (Central Statistics Office) and UNICEF, 2003) Child labor is also a key constraint, affecting 11% of children aged 6–18 years in rural areas, with significant disparities across households.11 A minority (about a third) of working children still go to school The issue is more acute for older children According to the Government of Afghanistan (Central Statistics Office) and UNICEF (2003), this number is slightly lower in urban areas (7%) and higher for boys than for girls (11% against 5%, for a national average of 8% of children 7–13 years that have worked at least half a day for income) In addition, 17% of children have domestic chores, as high as 23% of girls (11% of boys) and 19% in rural areas (children 7–13 years that did domestic chores worked at least half a day, Government of Afghanistan (Central Statistics Office) and UNICEF, 2003) 3.2.2 Households Household characteristics are also likely to impact upon enrollment In rural areas, the average size of a household is 7.3 (NRVA) For 88% of households, the household head is a man; in 98% (s)he has attended school but only in 25% of the cases does (s)he know how to write and read Even if, as pointed out in the 1990s by Nancy Dupree, ‘‘a great respect exists for the learned and for books’’ (World Bank, 1999), international experience suggests that literacy of the parents is an important factor In many countries, the household level of income or expenditure has an impact on enrollment To measure this, we use a notion of welfare based on consumption.12 Both primary and secondary enrol11 In the NRVA, child labor was defined as having worked for a pay over a recall period of days 12 A vector of food consumption for 11,227 households was obtained from the NRVA household survey This was then multiplied by a fixed vector of prices (a national median) to obtain a measure of expenditure on food that is the closest approximation to money metric utility available Non-food expenditure on education, medicine, clothing, taxes, fuel, and oil was then obtained from 5559 wealth group surveys within villages This was an estimate of the typical amount a household in one of three wealth groups in that village would spend on these items and was estimated by a gathering of household members from that wealth group (who for convenience were often later 427 ments are increasing with income, but primary enrolment is consistently higher and more evenly distributed than secondary enrollment In both cases, the enrollment differential between the richest and poorest is largest for boys than girls The primary enrollment rate is different only for households on the extreme of the expenditure distribution On the other hand, the more significant difference in secondary enrollment between the first and last deciles (4% and 10%, respectively) suggests that income might be a more important constraint at this level This is not to say that the constraint is the direct cost of education, as the cost could also be about the opportunity cost of older children not working 3.2.3 Communities Finally, there are various aspects of the communities, including their history and traditions, which will affect enrollment Most of these aspects are unobservable, in a statistical sense, not in an anthropological sense The local language is one of the factors that can be measured and it can be linked in broad terms to specific cultures It appears that the Pashtu-speaking schools (657,000 students), compared to the Dari-speaking schools (1,394,000 students),13 have: (i) lower enrollment overall and notably for girls; (ii) lower enrollment in middle schools, probably reflecting a more limited pool of student years ago; (iii) higher enrollment in Islamic schools; and (iv) higher student/teacher ratio (but slightly lower student/school ratio) (based on the SC) The correlation analysis (Table A1 in the annex) suggests that districts with a majority of Pashu-speaking schools tend to have: (i) lower primary enrollment; (ii) higher student per teacher ratios, but lower student per permanent teacher ratios; (iii) a smaller proportion of female schools and female teachers; but (iv) a higher proportion of schools under reconstruction (see above) Access to school (cf above and Table 4) also has a child, household, and community dimension Depending on the community, the cost of going to (footnote continued) chosen as sample households) Therefore there were between two and three data points which had the same non-food expenditures in each village A total expenditure measure was then calculated by summing food and non-food expenditure We thank David Atkin and Philippe Auffret for providing us with these data 13 This refers to the primary teaching languages The other 2,375,000 students are in schools with mixed languages or other primary languages ARTICLE IN PRESS 428 S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 school might change, with different risks (including of kidnapping) and costs (e.g transport) There might also be social or ethnic barriers as well, notably for girls walking alone and being teased by young boys (in Hunte, 2005, ‘‘we are Pashtun’’ is presented as a reason for not going to school) Depending on the household, these costs (the direct cost of transport and the foregone income of having a child in school) will be more or less binding Households will also look at their community’s behavior as a norm (Hunte, 2006) Finally, these costs will also vary with gender and age (in Hunte, 2005, a barrier for older girls is that ‘‘they are too old girls to go out of the household’’) Empirical estimates in rural areas We now turn to our regression analysis Let us first summarize the main correlates of primary enrollment (Table shows the marginal effect of each variable on enrollment.) Everything else being equal, this analysis suggests the following: Among child characteristics, age and gender are very important The likelihood of going to school for the very young (6) is, everything being equal, somewhat lower than those of age 7–11; after 12 years of age the likelihood of going to school declines quickly This reflects a mix of securityrelated and generational issues, that is, older children would have had to start school during the civil war or the Taleban period It is also possibly linked to the issue of child labor which is difficult to assess for econometric reasons (see next point) Being a boy increases the likelihood of going to school by almost 40% compared to girls Physically disabled children have a 14% lower probability to go to school, while mentally disabled children—who are much fewer—go to school 20% less often As discussed below, these results on age and gender reflect a mix of issues related to tradition and cultural norms, as well as lack of availability of girls’ schools and female teachers Given the endogeneity problems, we analyzed the impact of child labor in a separate regression Children who have been working and being paid are less likely to go to school But the coefficient is quite small (13%), reflecting that many children who work also go to school though this could have an impact on learning achievements Among household characteristics, size is important: children in large households have a significantly lower probability of sending their children to school Income differentials (measured by a notion of household expenditure) have no strong correlation to enrollment This suggests that: (i) all segments of the population place similar hopes in education; and (ii) cost might be important, but not a major driver Nevertheless, this conclusion is subject to some data limits: a large number of the poorest people have not answered the question on enrollment Our assumption to exclude the individuals with no answer from the analysis leads to this results—on the contrary, assuming that no answer means no enrollment would lead to a significant and positive impact of income (poorest people go less to school, see column 10 in the annex) There are other factors that are related to the economic environment and available opportunities of the households: orphans as well as children in a household without regular salary, especially those paid in kind, are less likely to go to school Participation of the household in some welfare program has no impact on enrollment Migration status has little impact on enrollment The only exception seems to be that households that have moved from one rural place in Afghanistan to another are less likely to send their children to school This could reflect that households have moved to find work or because of security reasons, and are hence less likely to send their children to school Contrary to a finding by Hunte (2006), our results suggest that, in rural areas, returned refugees not have a higher likelihood of enrolling in school Household environment also counts in the enrollment decision: children with parents who can read or write are more likely (5%) to school Children in a household with a radio are also more likely to go to school (8%), while TV has no impact The community environment counts as well Even taking into account many other factors, enrollment depends on the region, with the probability of enrollment lower in the Western region and higher in the Eastern and Southern regions Kuchis (the nomadic people in Afghanistan) have a clear disadvantage (likelihood 41% lower) Pasthu speakers are less likely to be enrolled (again, everything else being equal), with the ARTICLE IN PRESS S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 429 Table Summary regression analysis Primary enrollment Variables Child Age Age squared Boys Household Size (number) Head can read/write Activity other than regular salary Own a radio Migrant: most of the last years in other part of rural Afghanistan Community Kuchi Region (base ¼ East-central) Eastern North Eastern North Western Central Southern Language (base ¼ Dari) Pastho Security issues District level proportion of Dari speakers Schools Distance to primary school (base ¼ within community) At less than 1/4 day 1/4–1/2 days 1/2–1 days More than day Not available Operated by (base ¼ Government) Mosque Others Availability of teachers (at the district level) Higher teacher/school Share of contract teachers Gender issues (at the district level) Share of male schools Share of female teachers Pass rate Secondary enrollment Mean All Girls All Girls 9.70 105.08 0.50 0.36*** À0.02*** 0.38*** 0.34*** À0.02*** 0.09*** 0.00*** 0.26*** 0.02* 0.00* 8.39 0.34 0.08 À0.03*** 0.04** $À0.15** 0.00** 0.03*** $À0.03*** 0.00*** 0.00 0.51 0.06 À0.02*** 0.05*** À0.22 to À0.49*** 0.08*** À0.15*** 0.04** À0.06 0.02*** À0.01 0.01** 0.00 0.05 À0.41*** À0.31*** À0.05*** 0.00 0.28 0.03 0.20 0.13 0.01 0.12 0.17*** À0.01 À0.01 À0.07** 0.05 0.12*** 0.23*** À0.02 À0.04 À0.06 0.02 0.04 0.01 0.06*** À0.02** À0.02 0.11* 0.05* 0.00 0.07*** À0.01 0.00 0.06 0.03 0.52 0.05 33.9811 À0.11*** À0.09** 0.0014*** À0.15*** À0.08* 0.0027*** À0.02** 0.03* À0.0001 À0.01 À0.01 0.0001 0.34 0.06 0.01 0.00 0.11 À0.10*** À0.25*** À0.35*** À0.39*** À0.52*** À0.09*** À0.17*** À0.24** À0.28** À0.34*** 0.01 0.00 0.28*** 0.28** 0.44*** 0.13*** 0.03 À0.03 0.00 0.00 0.21 0.54 0.02* À0.08* 0.01 À0.15*** À0.01* À0.08*** 0.00 À0.03 0.42 0.06 0.63 À0.04 0.17** 0.15*** À0.21*** 0.30*** 0.28*** À0.01 À0.01 0.00 0.01 0.01 0.00 13,052 27.51 6024 23.98 4921 49.06 2103 26.43 Number of obs Pseudo R2 Mean and regressions on primary enrollment on 4–15-year-old children; regression on secondary enrollment on 10–18-year-old children All with survey weights Coefficients for are marginal effects (***), (**), and (*) mean significance at 1%, 5%, and 10%, respectively See details in annex (including variables not reported here) Source: Authors’ calculation, see Annex likelihood of enrollment being around 10% lower than all other speakers As we note below, this possibly reflects a lack of schools where children’s mother tongue is used (hence the con- cerned households just state that there is no school available for their child) This phenomenon is further strengthened by the impact of other members of the community on ARTICLE IN PRESS 430 S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 each household: everything being equal, enrollment is higher in districts with a higher proportion of Dari speakers Similarly, a higher enrollment rate in the district has a positive externality on individual decisions (given the correlation between this variable and other exogeneous variables, we tested this assumption in a separate regression) As noted by Hunte (2005), households take into account their neighbors’ enrollment decisions to make their own decisions Security is also an important barrier to enrollment: enrollment is 9% less likely in for households that have mentioned a ‘‘security incident’’ in the previous year Finally, the availability and quality of schools is an important driver of enrollment Proximity is very important (even if not in the community, it should be at least at less than 14 day from the community), although it should be noted that in 80% of cases there is a school in the community or within 14 day The availability of secondary schools also has some impact on primary enrollment Similarly, a high proportion of rural schools in a district (compared to the total number of school in the district) is positively correlated with enrollment Reconstruction of schools seems to have no impact on enrollment—but, as noted above, the quality of this variable is poor Enrollment is higher for schools not operated by the Government (likelihood higher by 0.28), even though there are too few of these schools in the sample (1%) to be definitive Finally, there are several factors that underscore the importance of the quality of teaching First, a high proportion of contract teachers (presumed less experienced than permanent teachers, even though we saw above that their formal qualifications are similar) reduces the likelihood of enrollment A higher pass rate in the district increases the probability of enrollment (although it also means that more children stay in school, hence introducing some reverse causality) In terms of gender, a high proportion of female teachers increases the likelihood of enrollment, while the proportion of male-only schools has little impact In terms of magnitude of the correlates, gender is one of the most influential factors for enrollment Among the large negative factors are the size of the family, being a Kuchi, having lost one’s parents, being in a household without regular salary, or being located in a community without a school or in a district with a low proportion of female teachers Mentally disabled also have a markedly lower likelihood of going to school Going to a non-government school also has a large positive impact, but it is rare as noted above In addition, we performed the same regression analysis on two sub-samples.14 For girls (Table 4), the results are very similar overall to the main regression The main nuance is the even more central role of quality: the share of male schools in the district, the proportion of female teachers, the share of contract teachers, and the pass rate all have much stronger effect than in the regression with the whole sample Besides, regional disparities are less pronounced, with only the Eastern regions significantly different from others (with higher likelihood of enrollment) Some of the environment factors, such as the proportion of Dari speaker in the district, play even more than for boys Management by non-government seems to have, at the margin, a slightly stronger (positive) impact on girls’ enrollment (which comes somewhat as a surprise given that the result applies for schools managed by the mosque, while we have seen above that girls’ enrollment in these schools is low) On the other hand, the source of income (regular salary or not) was less correlated with girls enrollment Also, the lower likelihood of Kuchis enrolling their children is slightly less marked for girls We finally looked at the 6–7-year-old age cohort, as these are the children whose progress through the system might have been less impacted by the civil war (since they were not supposed to be in school before 2001) A sign of hope is that the lower likelihood of girls being enrolled is much less pronounced in this subsample (although this might just reflect the tradition factor that makes it more difficult for older girls to leave the household), and 14 We also run some sensitivity analysis First, the main regression was done through different statistical specifications While probit is the preferred approach, tests with a LPM and a logit lead to largely consistent results Second, changing some variables led to similar results We tested the use of dummies for each age, instead of a continuous quadratic function We also tested subjective wealth categories instead of consumption-based quintiles We also performed the regressions on smaller samples, as indicated in the text All these specifications led to similar results, reducing the risk of a major systematic measurement bias ARTICLE IN PRESS S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 there are much lower regional disparities Also, many of the household and community factors have less impact And as for the previous two regressions, management by a NGO has a strong positive impact (a conclusion that needs to be nuanced by the small sample available for these schools) A complex empirical issue is related to the children, for which we not have an answer to the question on enrollment The standard approach is to exclude them from the sample, but this could introduce a sample bias if non-answer is related to some factors correlated with the decision not to enroll As a sensitivity analysis, we looked at the impact of assuming that non-response is equivalent to non-enrollment Many of our results are robust to this change, except mainly two First, income becomes a positive factor (higher income plays a positive role in the enrollment decision): this would be consistent with the assumption that the poorest not answer the enrollment questions and is likely to introduce a bias We conclude that income indeed plays a role in enrollment even if our main regression does not show that, but the role is not very large Second, the Southern region now has a lower than average enrollment likelihood We also analyzed the reasons given for nonenrollment (in the NRVA) For this part, we restricted our sample to all individuals that are not enrolled and we regressed the reasons that households gave for non-enrollment on the same variables than above (child, household, community, and school characteristics).15 Let us focus on the two regressions related to the two main reasons for non-enrollment: In 42% of the cases, households claim that enrollment would be ‘‘contrary to a family commitment, the child’s marriage, or their tradition’’ This reason is most often used for girls (age makes little difference, but this is more likely to apply to older children) Literate parents 15 The use of these data raises two technical complexities First, these answers cannot be used in our main specification since households answer them only in cases where the child was not going to school Hence these data are only available for the subsample of non-enrolled children Second, using them, we must keep in mind that households had to give one answer out of 11 choices, hence these variables are mutually exclusive As a result, for instance, physically disabled children appear less likely to give the ‘‘family commitment/marriage/tradition’’ answer as an explanation for not going to school, which is likely to mainly reflect that they will give the ‘‘health/disability’’ answer Similarly for security issues 431 are less likely to give this answer So are households close to a school run by a mosque or households in a district with a high proportion of male schools Households returning from Pakistan are more likely than others to mention this reason This reason is given more often in the Southern region, less in the North/North Eastern/Eastern regions In 30% of the cases, households indicate that there is no school available This correlates well with our measure of distance to school Everything else being equal, this is likely to be more of an issue for girls, younger children, mentally disabled, and children in richer households Households that had recently migrated (notably from urban areas or from countries other than Pakistan) are also more likely to raise this issue, possibly due to their high expectations Kuchis and non-Dari speakers are also more likely to raise this issue, everything else being equal: this probably reflects their expectation to have, in the community or close by, not only a school but also a school teaching in their language Finally, we looked at secondary enrollment (Table 4) The drivers are very similar to primary enrollment Surprisingly, everything else being equal, the impact of gender is somewhat smaller than for primary education As in the case of primary education, income differentials have no significant impact; however, households growing poppy seem to be slightly more likely to send their children to secondary school The regional dummies have a different profile, with smaller disparities across regions Distance to schools seems to have a less powerful impact Conclusions In this article, we have first documented the dramatic increase in enrollment over the last years While we not have data to fully ascertain the drivers of this increase, this paper identifies some of the factors that correlate to enrollment in rural areas In particular, lower security risks seem to have increased the likelihood of going to school The increase in the number of teachers was associated with enrollment; however, it is significantly reduced by issues of quality; for example, the correlation with contract teachers is less significant School construction seems to have had a very marginal relation to enrollment But, overall, our À0.0012 0.0241 À0.0213 À0.1199* 0.0331 À0.0822 0.1626* 0.1155* 0.0499 0.0606 À0.3739* 0.021 0.0298 0.1797* 0.2967* À0.3523* 0.0435 À0.0463 0.1571* À0.1179* 0.4191* 0.1955* 0.2286* 0.1842* À0.2994* 0.0432 0.0357 À0.0791 0.0395 0.0647 0.2616* 0.3208* 0.0678 À0.0511 0.2671* 0.1312* 0.1544* À0.0919 À0.0086 0.0842 À0.1051 À0.1338* 0.3798* 0.0331 À0.0586 0.0006 À0.2183* 0.0375 À0.0051 0.0782 À0.0252 À0.2149* 0.0607 0.05 À0.0441 0.0712 À0.0525 All student over permanent teacher À0.3399* 0.0469 À0.1719* 0.0412 À0.3737* À0.1942* À0.1454* 0.1984 0.1210* All student over all teacher 0.1760* % Pashto speaking people À0.0993 À0.6957 Source: SC and NRVA, authors’ calculations Primary enrolment rate Middle school enrolment rate % Dari speaking people % Pashto speaking people All student over all teacher All student over permanent teacher Female school rate Rural school rate Girl student rate Female permanent teacher rate Female teacher gross rate Female contract teacher rate Contract teacher rate Higher teacher/school ratio Reconstruction school rate Primary Middle % Dari enrolment school speaking rate enrolment people rate Table A1 Correlation matrix at the district level Rural school rate Girl student rate À0.045 À0.0068 À0.0136 0.3306* 0.3907* Female permanent teacher rate À0.0008 0.021 À0.2673* À0.2091* À0.0391 0.0827 0.4416* À0.1007* 0.5336* À0.2137* 0.3029* À0.0064 À0.0648 0.5248* 0.4616* À0.1522* 0.6171* À0.1587* 0.2858* À0.1204* 0.2901* Female school rate Female contract teacher rate Contract teacher rate 0.083 0.0134 À0.2599* 0.2542* Higher Reconstruction teacher/ school school rate ratio À0.1014* À0.1781* 0.1951* 0.0203 À0.2204* 0.6982* Female teacher gross rate 432 S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 ARTICLE IN PRESS ARTICLE IN PRESS S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 regression analysis cannot account for the whole increase in enrollment This obviously reflects a deeper structural change with the end of major conflict and an increase in the demand for education linked to the sudden change in potential returns to schooling, itself linked to security, large inflows of external assistance, and more broadly to economic growth We have also described the massive gender gap Our analysis emphasized the role of female teachers and girls’ schools, as well as the specific challenges of secondary education Finally, our analysis stresses the importance of quality in education Although we still measure this poorly, factors such as the share of contract teachers or the share of teachers with higher education tend to have higher correlation with enrollment To make further progress toward universal enrollment, this analysis suggests a number of opportunities First, demand for school still remains linked to the household context There is therefore a role for promoting schooling, notably to reach illiterate heads of households A possible approach is also to increase management of schools by nongovernment organizations which have good track record in providing social sector services, preferably education: although our data are too limited to test this adequately, they seem to indicate that in Afghanistan, as in other countries, there is scope for devolving management to local communities, an area where cautious piloting and evaluation could be initiated Access to school is important, notably for primary education, but reconstruction of schools has a marginal correlation with enrollment Similarly, it is unclear that more teachers would have a major direct impact on enrollment if teacher training does not expand Of course, history—in Afghanistan, as well as the experience of other countries in their progress toward universal enrollment—calls for modesty And beyond this most urgent challenge of universal primary enrollment are two other critical challenges, the quality of education and the provision of secondary education We have noted some positive features for the secondary level (such as a higher proportion of female teachers and of better-educated teachers), but this is a sector that will need significant resources in the future Beyond these conclusions, additional research will be needed to better understand the drivers of enrollment and how to target public interventions In particular, the role and impact of management by 433 non-government organizations needs to be further investigated, as this is potentially a key policy to scale up enrollment (and most likely quality) More careful monitoring also is of primary importance At the household level, the 2005 NRVA will be a major step forward, with a more complete questionnaire and better and expanded—to urban areas—sampling At the administrative level, there is a need to monitor schools—human resources, performance, management—and to better track resource flows, possibly through a Public Expenditure Tracking Survey building on the one utilized in this paper Appendix A1 Annex: detailed results See Table A1 References Bedi, A.S., Marshall, J.H., 2002 Primary school attendance in Honduras Journal of Development Economics 69, 129–153 Bruns, B., Mingat, A., Rakotomalala, R., 2003 Achieving Universal Primary Education by 2015—A Chance for Every Child The World Bank, Washington, DC Buckland, P., 2005 Reshaping the Future: Education and Post Conflict Reconstruction The World Bank, Washington, DC Clemens, M.A., 2004 The long walk to school: international education goals in historical perspective Center for Global Development Working Paper, 37 Evans, A., Manning, N., Osmani, Y., Wilder, A., 2004 A Guide to Government in Afghanistan World Bank and AREU, Washington, DC Glewwe, P., Kremer, M., 2005 Schools, teachers, and education outcomes in developing countries Bread Policy Paper, Government of Afghanistan (Central Statistics Office) and UNICEF, 2003 Moving Beyond Decades of War: Progress of Provinces: Multiple Indicator Cluster Survey 2003 Afghanistan Kabul Government of Afghanistan, 2006 Interim Afghanistan National Development Strategy, draft Kabul Human Rights Research and Advocacy Consortium, 2004 Report Card: Progress on Compulsory Education (Grades 1–9), March 2004 Hunte, P., 2005 Household decision-making and school enrollment in Afghanistan: case studies in Chahar Asyab District, Kabul Province; District 13 Pu-i-Khishk, Kabul City; Nesher Villages Belcheragh District, Faryab Province, District 2, Kandahar City AREU Working Papers, December 2005 Hunte, P., 2006 Looking beyond the school walls: household decision-making and school enrollment in Afghanistan AREU Briefing Paper, March 2006 Rashid, F., 2005 Education and gender disparity in Afghanistan Master Thesis in Development Economics, Williams College, Massachusetts The Guardian, 2006 Fears of lost generation of Afghan pupils as Taliban target schools March 16, 2006 ARTICLE IN PRESS 434 S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 WFP, USAID/APEP, Save the Children USA, 2004 Results and discussion of education data collected in the Afghanistan National Risk and Vulnerability Assessment 2003 Mimeo Wilder, A., 2005 A house divided? Analyzing the 2005 Afghan Elections AREU Working Paper, December 2005 World Bank, 1999 Education for Afghans World Bank Workshops’ minutes World Bank, 2005 Investing in Afghanistan’s future Report No 31563-AF, Washington, DC World Bank, 2006 Afghanistan: managing public finances for development Report No 34582-AF, Washington, DC ... International Journal of Educational Development 28 (2008) 419–434 ARTICLE IN PRESS ARTICLE IN PRESS S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 regression... UNICEF, 2003) 3.1 The supply of education The Government’s vision of the education sector, set forth in a number of policy documents, is to provide good quality education for all regardless of... led to similar results, reducing the risk of a major systematic measurement bias ARTICLE IN PRESS S Guimbert et al / International Journal of Educational Development 28 (2008) 419–434 there are