research topics on girl child education

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• Yale J Biol Med
• v.93(4); 2020 Sep

Focus: Sex & Reproduction

A case for girl-child education to prevent and curb the impact of emerging infectious diseases epidemics, shadrack frimpong.

a Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT

b Department of Pediatrics, Yale School of Medicine, New Haven, CT

Elijah Paintsil

Not only do epidemics such as HIV/AIDS, Ebola Virus Disease (EVD), and the current Coronavirus Disease (COVID-19) cause the loss of millions of lives, but they also cost the global economy billions of dollars. Consequently, there is an urgent need to formulate interventions that will help control their spread and impact when they emerge. The education of young girls and women is one such historical approach. They are usually the vulnerable targets of disease outbreaks – they are most likely to be vehicles for the spread of epidemics due to their assigned traditional roles in resource-limited countries. Based on our work and the work of others on educational interventions, we propose six critical components of a cost-effective and sustainable response to promote girl-child education in resource-limited settings.

Introduction

“Study after study has taught us that there is no tool for development more effective than the education of girls. No other policy is as likely to raise economic productivity, lower infant and maternal mortality, or improve nutrition and promote health – including the prevention of HIV/AIDS.”

Kofi A. Annan, former Secretary-General, United Nations [ 1 ].

As it has been the case of HIV/AIDS and, most recently, the Ebola Virus Disease (EVD), the ongoing Coronavirus Disease 2019 (COVID-19) is a stark reminder of a painful reality: epidemics will continue to be the bane of human existence. Consequently, there is an urgent need to critically assess the literature of approaches that have previously mitigated outbreaks and design robust interventions to fight these emerging outbreaks. One such method is the education of young people, especially girls and women. The fact that education is an essential social determinant of health has been well documented [ 2 ]. Early childhood education provides access to higher-income earning potentials, reducing one’s likelihood of getting infected by disease during an epidemic [ 3 ]. The benefits of education in improving health outcomes are high in children and young people, who often depend on their parents and guardians for their livelihoods, including financial resources to access education [ 1 , 3 ]. For instance, a study in Ghana found that the higher the level of education a mother has, the better the child’s chances of survival [ 4 ]. Also, according to UNICEF, young boys and girls who have higher education levels usually have more knowledge about infectious diseases, are less likely to get infected, and tend to adopt behaviors and attitudes that prevent them from being infected [ 1 ]. These health-related benefits of education become even more critical in epidemics such as HIV/AIDS and EVD. Indeed, several studies from around the world have confirmed that HIV infection rates are at least twice as high among adolescents who drop out of primary school than those who stay in school [ 3 ]. The 2014 West African EVD outbreak killed over 11,000 people, left many children orphaned, and exposed to malnutrition, hunger, and preventable deaths [ 5 ]. Deeply affected by the negative repercussions of the lack of quality education, young girls and women in low- and middle-income countries (LMICs) usually must contend with power dynamics, traditional roles, and social inequities in communities [ 1 ]. While the global community has made significant headway over the years to leverage access to education as a tool to enhance health outcomes for young girls and women, there still exist gaps in addressing health challenges for this vulnerable population in many LMICs across the globe [ 3 ]. Prior educational interventions have often failed to adequately engage the communities in which they operate or are not financially sustainable without long-term donor support [ 3 ]. Consequently, they crumble during epidemics when donor funding becomes limited, schools close, and teachers and administrators die or become sick. Financially sustainable and community-driven educational interventions for young girls can help to address these challenges and improve health outcomes in LMICs and help curb epidemics.

Impact of Global Educational Efforts on Improving Health Outcomes

Given the limited resources available for tackling the myriads of global health challenges, we must assess prospective educational interventions in the light of rigorous evidence before implementing them. Consequently, we ask: how exactly have global education efforts to improve health performed so far? Evidence from published literature sheds some light. In a study of eight sub-Saharan African countries, Gupta et al . found that females who had eight or more years of schooling were 13% more likely to avoid sex before age 18 compared to their peers with less education [ 6 ]. Also, in Zimbabwe, studies have confirmed that among 15-18-year-old adolescent girls, those who drop out of school are about five times more likely to have HIV than their colleagues who stay in school [ 1 ]. Evidence from surveys in Malawi, Haiti, Uganda, and Zambia further corroborates these findings by showing a secure link between higher education and fewer sexual partners [ 7 ]. The general observation here is that these outcomes are not limited to one country. Indeed, Kirby et al. also found that, in 11 countries, women with some form of schooling were about five times more likely to have used condoms during sexual encounters than uneducated women in similar backgrounds and settings [ 8 ]. These findings underscored global commitments such as the Millennium Development Goals (MDGs) related to HIV/AIDS, Education and Girls; the Dakar Framework for Action related to Girls’ Education; and the current Sustainable Development Goals (SDG #4) to promote inclusive and equitable quality education for all [ 1 ]. Based on these initiatives, many LMICs have made commitments to improving girl-child education. In Ghana, the early 2000s was the era of media campaigns dubbed “send your girl child to school” [ 9 ].

Similarly, many other African governments, with the help of international partners, joined these laudable efforts by providing hundreds of millions of dollars to establish schools for girls and to embark on public education programs in rural communities [ 9 ]. A natural follow-up question would be; how have these educational commitments and interventions affected health outcomes so far? In a recent systematic review, Psaki et al . concluded that, even though investments in schooling may yield positive ripple outcomes for sexual and reproductive health, these effects may not be as pronounced as expected [ 10 ]. In a similar light, Mensch et al . also concluded in their review that, while improvements in women’s educational outcomes such as grade attainment have helped to improve health in several places, the effect may be overestimated [ 11 ]. Regardless, these two studies highlight the critical roles that educational quality and an intervention’s implementation level (community versus national) might play in the synthesis of these outcomes and the conclusions they reported.

The “Ever-Widening Gap” Burden of Epidemics

Although there is a wealth of evidence that education is essential for preventive health in the population, the global community failed to meet the MDG #3 of equal access to education for girls by 2015. Why then are educational efforts still failing to address the health challenges of young girls and women, as we have observed in the HIV/AIDS epidemic and, most recently, the EVD crisis [ 12 ]?

The answer to this question may stem from the challenges of financial sustainability and community engagement that these epidemics expose. This answer corroborates the caveats that Mensch et al . (2019) and Psaki et al . (2019) highlighted. For instance, during the HIV/AIDS and recent EVD outbreaks, many young girls had to drop out of school to provide and care for their families who were sick and dying, consequently increasing their risk of exposure to these lethal viruses [ 13 ]. In these situations, when parents or guardians died, these girls dropped out of school due to default in payment of school fees. In 2003, there were as many as 15 million AIDS orphans [ 1 ], and the 2014 West African EVD outbreak left tens of thousands of orphans in its wake [ 14 ]. Faced with a compounded challenge of affording their nutritional and accommodation needs, they engaged in risky transactional sex as a means of survival [ 14 ].

In some cases, these were non-consensual sexual encounters. Evidence from the Children’s Ebola Recovery Assessment of 617 girls in Sierra Leone found that many young girls who had dropped out of school ended up in Ebola-quarantined households where they became subjects of rape and other forms of sexual assault [ 15 ]. Moreover, the United Nations Population Fund (UNFPA) reported that many young girls in the Democratic Republic of Congo encountered rape and sexual attacks from members of armed groups in communities such as North Kivu and Ituri [ 16 ].

Beyond issues of gender-based violence and school drop-outs, epidemics also weaken the capacity and quality of educational systems. In many countries that are hard-hit by HIV/AIDS and EVD, many teachers died, and the few that survived also had to suspend their teaching roles to cater for their sick and dying family members [ 1 ]. In 2000, at the peak of the HIV/AIDS crisis, about 815 of primary school teachers (45% of trained teachers) in Zambia died from the disease, and close to 8% of the 300 teachers in the Central African Republic also died [ 1 , 17 ]. These teacher shortages are prevalent in rural areas where schools lose staff. Teachers who are affected by disease outbreaks migrate to urban areas for better healthcare for themselves or concerned family members in their care [ 1 ]. In Malawi, the student-teacher ratio in many schools jumped to about 96 to 1 due to AIDS-related illnesses [ 18 ].

Making a Case for Sustainable Education Models

Worldwide, 13 out of the 15 countries where over 30% of school-aged girls are out of school, are in Sub-Saharan Africa [ 19 ]. Yet, this same population of girls remains the most vulnerable during pandemics when schools are closed. In a recent comprehensive report, the Malala Fund forecasted that there is a high likelihood that more than 10 million secondary school-aged girls in LMICs may not return to school after the COVID-19 pandemic [ 20 ]. There is a need for educational interventions for young girls that can withstand the financial shortfalls and the capacity-weakening effects on educational systems that pandemics cause. An organization that implements such interventions is Cocoa360, a nonprofit in rural Western Ghana which transforms communities by using revenues from existing community resources such as cocoa, to finance educational and health outcomes ( Figure 1 ). The details concerning Cocoa360’s model and its impact have already been discussed in a separate publication [ 21 ]. Herein, we draw on published evidence and our own experiences with Cocoa360 to propose six general components ( Figure 2 ) that should be considered in insulating educational models from epidemics:

Cocoa360’s Innovation: “Farm-for-Impact” Model.

General Components of Sustainable Educational Models.

Active community engagement : In a review of interventions that improve girls’ education and gender equality, Unterhalter et al . highlight the effectiveness of programs that focused on shifting gender norms and encouraging inclusion through community involvement [ 22 ]. This finding matches our experience and shared belief that the currency for successful interventions in sub-Saharan Africa is a strong community engagement. Working together with community leaders and members enables external project partners to appreciate cultural nuances and local contexts that need to be considered before, during, and after the implementation process. In many rural communities, community leaders and members coordinate mutual support such as construction labor, contribute resources such as land, and are willing to engage in initiatives that will help financially sustain the intended interventions over the long term. For an education model to be sustainable, it would similarly need to include the inputs and perspectives of the residents and their leaders right from the planning phase, throughout the implementation phase, and post-implementation evaluation efforts as well. Cocoa360, a nonprofit organization in rural Western Ghana, provides an example of how to solicit and successfully engage a community. As a community-based organization, Cocoa360 transforms rural communities by using revenues from existing community resources such as cocoa to finance educational and health outcomes [ 21 ].

From our experience with community engagement, the following are essential ingredients for successful community engagement: (1) Community “knocking” – this is the first step in entering a community. Use this period to share your concept and vision about the project with community elders, usually, the chief, for their acceptance and input; (2) Shared leadership – the community should be involved in the governance of the project right from the start. Cocoa360 collaborated with their partner communities to establish a local decision-making body called the Village Committee (VC) [ 21 ]. This group comprises some of the community’s respected citizens from diverse religious, ethnic, and occupational backgrounds. The VC serves as a link between Cocoa360 and the broader community and ensures that Cocoa360’s operations, including those at its Tarkwa Breman Girls School and Tarkwa Breman Community Clinic, reflect the needs and cultural standards of the community and its members [ 21 ]. This alignment is especially important because many such rural communities still do not prioritize girl-child education due to persisting cultural beliefs; (3) Community education – the community should be reminded continually of the components, their role in the project’s successes, and challenges. Such educational efforts will deepen buy-in and ensure the continued success of the program; (4) Shared successes – the project’s progress should be celebrated with the community in such a way that they know and feel that their contributions matter; and (5) Sustainability plan – the community should be involved in formulating strategies that will ensure project longevity.

Zero tuition costs : Efforts to eliminate tuition costs and make education compulsory in many countries have helped increase educational access for young girls, significantly reduced their vulnerability to child marriages, and many sexually transmitted diseases such as HIV/AIDS. For instance, eliminating tuition fees has been found to reduce child marriages in eight countries in Sub-Saharan Africa, including Ghana, Ethiopia, and Rwanda, despite the challenges encountered in the implementation of this policy [ 23 ]. Additionally, the government of Ghana’s Free Compulsory Universal Basic Education (FCUBE), which aimed to provide tuition-free education for all students in public primary schools, has led to a marked increase in enrollment rates [ 24 ]. In countries such as Kenya, Uganda, and Tanzania, eliminating tuition fees has helped to improve school enrollment and student attendance rates, particularly for young girls [ 3 ]. In Uganda, the government’s efforts at easing the burden of tuition led to a 30% increase in girls’ enrollment in school with an almost double effect for the poorest economic fifth of girls [ 3 ]. In a systematic review of 35 studies from 75 reports, Baird et al . also substantiated such findings of the impact of financial incentives on educational outcomes [ 25 ]. They found that both conditional and unconditional cash transfer programs improved the likelihood of school attendance and enrollment compared to programs without cash transfers [ 25 ]. With that said, it is essential to note that zero tuition costs come at a cost to a nation. In response, several governments have devised ways of domestically financing such interventions via taxation and innovative funding models [ 26 ]. Public-private partnerships models for funding primary and secondary education should be encouraged.

Zero non-tuition expenses through community-led revenue generation : As Koski et al . have shown, removing the cost of tuition alone would not be enough to reduce child marriage since other barriers to school enrollment may persist [ 23 ]. In Ghana, despite the FCUBE, there are still gender disparities in educational access. Many young girls are not benefitting from the scheme, and these probably may be due to financial difficulties related to the cost of textbooks, school uniforms, and transportation, which disproportionately plague marginalized households in rural communities [ 24 ]. Stack et al . found in a study in rural Western Ghana that in the face of financial challenges, 44.5% of heads of households, typically males, opted for the male child against 27.7% who chose the female child. Of these, 26.5% stated that it would depend on the specific circumstances, while 1.3% refused to answer [ 27 ]. Thus, the contribution of non-tuition expenses towards school attendance is not trivial. Community and private initiatives could take this burden off parents and heads of households. A thriving community initiative like this is the Cocoa360’s “farm-for-impact” model. Community members assist with work on a community-run cocoa farm in exchange for tuition-free education and subsidized healthcare. The revenues from the farm are applied to finance educational and healthcare services in rural communities in Ghana [ 21 ]. Evidence from meta-analysis and systematic reviews has suggested approaches such as Conditional Cash Transfers and bonuses as effective incentives for increasing girls’ school enrollment in developing countries [ 1 , 28 ]. While successful, these approaches rely on external funding to meet non-tuition expenses, which may not always be available. On the other hand, interventions like Cocoa360’s “farm-for-impact” model seek to consistently keep communities at the forefront of decision making and revenue-generation.

Consequently, they provide a much better independent pathway to self-support the education of their daughters when donor funding becomes limited or ceases [ 21 ]. The process of community-led revenue generation and decision making further imbues a strong sense of communal ownership, which can translate into educational outcomes. For instance, preliminary findings from Cocoa360 show that the school attendance rate is 98%, compared to the national rural attendance rate of about 70% for similar schools under the Ghana government’s Free Compulsory Universal Basic Education (FCUBE) program [ 21 ].

School-based food and nutrition initiatives : Epidemics such as HIV/AIDS and EVD cause many young girls to drop out of school when their parents and guardians are affected or die. Consequently, they end up resorting to transactional sex to obtain support for food and accommodation [ 1 ]. Even for families that remain intact without any deaths during outbreaks, school-based food programs may ease the burden of feeding. This challenge occurs because many families are generally in great need of food during epidemics, as many farmers fall sick and agricultural productivity and output may suffer as a result. Indeed, the World Food Program (WFP) found that in some places, when families were incentivized with food rations for sending their daughters to school, enrollment of girls in school tripled [ 29 ].

Health-centric and school-based curriculum : The instruction of health habits, hygiene, and sanitary conditions and local cultural contexts and their impact on health behaviors should not just be a footnote in educational programs. Instead, they should receive the same attention as math and reading. Right from an early age, schools should provide students with age-appropriate health education that will arm them with the knowledge, skills, and tools to take care of their health and be aware of risky health behaviors. In South Africa, such health-centric and life skills-based curricula have helped to improve young people’s odds of using condoms during sex [ 30 ]. Synthesis of established evidence also shows that school-based sexual health education has the potential to improve condom use among young people in Sub-Saharan Africa and to reduce the prevalence of Sexually Transmitted Infections such as Chlamydia [ 31 ]. Such comprehensive approaches to education can be achieved by ensuring that they are theory-driven, address social determinants like social norms, improve cognitive-behavioral skills, train facilitators like teachers, and include schools, families, and communities [ 32 ].

Safe and protected learning spaces : While schools serve as nurturing grounds for student learning, they can also expose young girls to gender-based violence (GBV) and negatively impact the attainment of educational objectives [ 33 ]. Several studies have reported a high prevalence of GBV, such as sexual harassment, unsolicited advances, touching, groping, and sexual assaults [ 34 , 35 ]. For instance, a Human Rights Watch (HRW) study in South African schools revealed that teachers raped young girls in empty classrooms, lavatories, and dormitories [ 35 ]. In Dodowa, Ghana, similar findings of sexual assault of young girls in schools have also been documented [ 34 ]. Such instances of school-based GBV make schools less attractive places for young girls, who consequently struggle to concentrate on their academics and may, therefore, drop out [ 36 ]. For other deterred girls, they may not enroll at all. Schools pursuing sustainable educational models should strive to make their learning environments safe for young girls by working with community leaders, teachers, students, and related government stakeholders to implement preventive and punitive measures for perpetrators. Evidence from systematic reviews highlights the role of interventions such as life skills training programs and positive peer relationship training approaches to promote self-esteem and to reduce bullying and rape [ 37 ]. Activities such as drama and arts can also help young people evaluate their gender roles and the steps they can take to ensure that their study spaces are safe and conducive to learning [ 1 ]. Successfully achieving this goal would require seeing boys as crucial partners to the solution, rather than external opponents.

Education has proven to be an effective intervention for improving health outcomes and reducing the spread and impact of epidemics. While the global community has made significant progress in leveraging quality girl-child education as a tool to enhance health in LMICs, challenges such as financial bottlenecks continue to impede such efforts, especially during outbreaks. Sustainable educational models that prioritize active community engagement, abolish tuition and non-tuition expenses, incorporate school-based food programs, infuse health-centric and skill-based curriculum, and promote safe learning spaces are much needed. Successful implementation of such efforts would significantly improve educational access and health outcomes for young girls and, consequently, provide long-lasting approaches to fight the spread and impact of epidemics when they emerge.

Acknowledgments

We are grateful to Priya Bhirgoo for her assistance with helpful comments and edits.

SF is the founder of Cocoa360.

• Girls H. IV/AIDS, and Education [Internet]. UNICEF; 2004. [cited 2020 Mar 3]. Available from: https://www.unicef.org/publications/files/Girls_HIV_AIDS_and_Education_(English)_rev.pdf
• Marmot M. Social determinants of health inequalities . Lancet . 2005. March; 365 ( 9464 ):1099–104. Available from: https://www.thelancet.com/journals/lancet/article/PIIS01406736(05)711466/fulltext 10.1016/S0140-6736(05)71146-6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• UNAIDS Fight AIDS [Internet]. 2006. May [cited 2020 Mar 3]. Available from: https://www.unaids.org/sites/default/files/media_asset/jc1185-educategirls_en_0.pdf
• Quansah E, Ohene LA, Norman L, Mireku MO, Karikari TK. Social Factors Influencing Child Health in Ghana . PLoS One . 2016. January; 11 ( 1 ):e0145401. 10.1371/journal.pone.0145401 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Decroo T, Fitzpatrick G, Amone J. What was the effect of the West African Ebola outbreak on health program performance, and did programs recover? Public Health Action . 2017; 7 ( Suppl 1 ):S1–S2. 10.5588/pha.17.0029 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mahy M, Gupta N. Sexual Initiation Among Adolescent Girls and Boys: Trends and Differentials in Sub-Saharan Africa [Internet] . Archives of Sexual Behavior . Kluwer Academic Publishers-Plenum Publishers; 2003. [cited 2020 Mar 3]. Available from: https://link.springer.com/article/10.1023/A:1021841312539 [ PubMed ]
• Gregson S, Nyamukapa CA, Garnett GP, Wambe M, Lewis JJ, Mason PR, et al. HIV infection and reproductive health in teenage women orphaned and made vulnerable by AIDS in Zimbabwe . AIDS Care . 2005. October; 17 ( 7 ):785–94. 10.1080/09540120500258029 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kirby D, Short L, Collins J, Rugg D, Kolbe L, Howard M, et al. School-based programs to reduce sexual risk behaviors: a review of effectiveness . Public Health Rep . 1994. May-Jun; 109 ( 3 ):339–60. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Wolf S, Mccoy DC, Godfrey EB. Barriers to school attendance and gender inequality: empirical evidence from a sample of Ghanaian schoolchildren . Res Comp Int Educ . 2016; 11 ( 2 ):178–93. 10.1177/1745499916632424 [ CrossRef ] [ Google Scholar ]
• Psaki SR, Chuang EK, Melnikas AJ, Wilson DB, Mensch BS. Causal effects of education on sexual and reproductive health in low and middle-income countries: A systematic review and meta-analysis . SSM Popul Health . 2019. May; 8 :100386. 10.1016/j.ssmph.2019.100386 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mensch BS, Chuang EK, Melnikas AJ, Psaki SR. Evidence for causal links between education and maternal and child health: systematic review . Trop Med Int Health . 2019. May; 24 ( 5 ):504–22. 10.1111/tmi.13218 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Menéndez C, Lucas A, Munguambe K, Langer A. Ebola crisis: the unequal impact on women and children’s health [Internet]. Lancet Glob Health . 2015. March; 3 ( 3 ):e130. Available from: https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(15)70009-4/fulltext 10.1016/S2214-109X(15)70009-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ngegba MP, Mansaray DA. Perception of students on the impact of Ebola virus disease. International Journal of Advanced Biological Research [Internet]. 2016. [cited 2020 Mar 9]:119–28. Available from: http://scienceandnature.org/IJABR_Vol6(1)2016/IJABR_V6(1)16-18.pdf
• The impact of Ebola on education in Sierra Leone [Internet]. World Bank Blogs . [cited 2020 Mar 3]. Available from: https://blogs.worldbank.org/education/impact-ebola-education-sierra-leone
• Children RI. Teenage Pregnancies in Ebola-Affected Sierra Leone [Internet]. World Vision . 2015. [cited 2020 Mar 3]. Available from: https://www.worldvision.org/about-us/media-center/children-report-increased-exploitation-teenage-pregnancies-ebola-affected-sierra-leone
• New Ebola outbreak hits women and girls hardest in the Democratic Republic of the Congo [Internet] . United Nations Population Fund . 2018. [cited 2020 Mar 3]. Available from: https://www.unfpa.org/news/new-ebola-outbreak-hits-women-and-girls-hardest-democratic-republic-congo
• UNESCO Education for All - The Quality Imperative [Internet] . Global Education Monitoring Report . 2004. [cited 2020 Mar 3]. Available from: https://en.unesco.org/gem-report/report/2005/education-all-quality-imperative
• Africa Bureau Brief United States Agency for International Development Bureau for Africa, Office of Sustainable Development ; 2002. [ Google Scholar ]
• Patel N, Jesse G. African states’ varying progress toward gender equality in education [Internet] . Brookings . Brookings; 2019. [cited 2020 Jun 8]. Available from: https://www.brookings.edu/blog/africa-in-focus/2019/06/13/african-states-varying-progress-towardgender-equality-in-education/
• Malala Fund Girls’ Education and COVID-19 ; 2020. April [cited 2020 Jun 8]. Available from: https://downloads.ctfassets.net/0oan5gk9rgbh/6TMYLYAcUpjhQpXLDgmdIa/dd1c2ad08886723cbad85283d479de09/GirlsEducationandCOVID19_MalalaFund_04022020.pdf
• Frimpong S, Russell A, Handy F. Re-Imagining Community Development: The Cocoa360 Model. Research Handbook on Community Development [Internet]. [cited 2020 Mar 3]; Available from: https://www.e-elgar.com/shop/gbp/research-handbook-on-community-development-9781788118460.html
• Unterhalter E, North A, Arnot M, Lloyd C, Moletsane L, Murphy-Graham E, et al. Interventions to enhance girls’ education and gender equality. Education Rigorous Literature Review . Department for International Development; 2014. [ Google Scholar ]
• Koski A, Strumpf EC, Kaufman JS, Frank J, Heymann J, Nandi A. The impact of eliminating primary school tuition fees on child marriage in sub-Saharan Africa: A quasi-experimental evaluation of policy changes in 8 countries . PLoS One . 2018. May; 13 ( 5 ):e0197928. 10.1371/journal.pone.0197928 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nudzor HP. Taking education for all goals in sub-Saharan Africa to the task . Manage Educ . 2015; 29 ( 3 ):105–11. 10.1177/0892020615584105 [ CrossRef ] [ Google Scholar ]
• Baird S, Ferreira FH, Özler B, Woolcock M. Conditional, unconditional and everything in between a systematic review of the effects of cash transfer programs on schooling outcomes . J Dev Effect . 2014; 6 ( 1 ):1–43. 10.1080/19439342.2014.890362 [ CrossRef ] [ Google Scholar ]
• Marcum-Mullins W. Conditional Cash Transfers for Education: A Comparative Analysis between the funder and country. International Development, Community, and Environment . IDCE; 2017. p. 102. [ Google Scholar ]
• Stack R. The Effects of an NGO Development Project on the Rural Community of Tarkwa Bremen in Western Ghana [Internet] . ScholarlyCommons . 2017. [cited 2020 Mar 3]. Available from: https://repository.upenn.edu/sire/47/
• Saavedra JE, Garcia S. Impacts of Conditional Cash Transfer Programs on Educational Outcomes in Developing Countries A Meta-analysis . RAND Labor and Population; 2012. pp. 1–63. 10.7249/WR921-1 [ CrossRef ] [ Google Scholar ]
• HIV/AIDS & Children Bringing hope to a generation, Food aid to help orphans and other vulnerable children . Rome: World Food Programme; 2003. [ Google Scholar ]
• Reddy P. Programming for HIV prevention in South African schools . Washington (D.C.): Horizons Research Summary, Population Council; 2003. [ Google Scholar ]
• Sani AS, Abraham C, Denford S, Ball S. School-based sexual health education interventions to prevent STI/HIV in sub-Saharan Africa: a systematic review and meta-analysis . BMC Public Health . 2016. October; 16 ( 1 ):1069. 10.1186/s12889-016-3715-4 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Peters LW, Kok G, Ten Dam GT, Buijs GJ, Paulussen TG. Effective elements of school health promotion across behavioral domains: a systematic review of reviews . BMC Public Health . 2009. June; 9 ( 1 ):182. 10.1186/1471-2458-9-182 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• RTI International RTI [Internet]. Literature Review on the Intersection of Safe Learning Environments and Educational Achievement . U.S. Agency for International Development . 2013. [cited 2020 Jun 8]. Available from: http://www.ungei.org/resources/files/Safe_Learning_and_Achievement_FINAL.pdf
• Afenyadu D, Lakshmi G. Adolescent sexual and reproductive health behavior in Dodowa, Ghana . Washington (D.C.): Centre for Development and Population Activities; 2003. [ Google Scholar ]
• Scared at School Sexual Violence Against Girls in South African Schools [Internet]. Human Rights Watch . 2009. [cited 2020 Mar 3]. Available from: https://www.hrw.org/report/2001/03/01/scared-school/sexual-violence-against-girls-south-african-schools
• Leach F, Dunne M, Salvi F. A global review of current issues and approaches in policy, programming, and implementation responses to School-Related Gender-Based Violence (SRGBV) for the Education Sector . UNESCO; 2014. [ Google Scholar ]
• Xu T, Tomokawa S, Gregorio ER, Jr, Mannava P, Nagai M, Sobel H. School-based interventions to promote adolescent health: A systematic review in low- and middle-income countries of WHO Western Pacific Region . PLoS One . 2020. March; 15 ( 3 ):e0230046. 10.1371/journal.pone.0230046 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

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Girls' education, gender equality in education benefits every child..

• Girls' education
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Investing in girls’ education transforms communities, countries and the entire world. Girls who receive an education are less likely to marry young and more likely to lead healthy, productive lives. They earn higher incomes, participate in the decisions that most affect them, and build better futures for themselves and their families.

Girls’ education strengthens economies and reduces inequality. It contributes to more stable, resilient societies that give all individuals – including boys and men – the opportunity to fulfil their potential.

But education for girls is about more than access to school. It’s also about girls feeling safe in classrooms and supported in the subjects and careers they choose to pursue – including those in which they are often under-represented.

When we invest in girls’ secondary education

• The lifetime earnings of girls dramatically increase
• National growth rates rise
• Child marriage rates decline
• Child mortality rates fall
• Maternal mortality rates fall
• Child stunting drops

Why are girls out of school?

Despite evidence demonstrating how central girls’ education is to development, gender disparities in education persist.

Around the world, 129 million girls are out of school, including 32 million of primary school age, 30 million of lower-secondary school age, and 67 million of upper-secondary school age. In countries affected by conflict, girls are more than twice as likely to be out of school than girls living in non-affected countries.

Worldwide, 129 million girls are out of school.

Only 49 per cent of countries have achieved gender parity in primary education. At the secondary level, the gap widens: 42 per cent of countries have achieved gender parity in lower secondary education, and 24 per cent in upper secondary education.

The reasons are many. Barriers to girls’ education – like poverty, child marriage and gender-based violence – vary among countries and communities. Poor families often favour boys when investing in education.

In some places, schools do not meet the safety, hygiene or sanitation needs of girls. In others, teaching practices are not gender-responsive and result in gender gaps in learning and skills development.

Gender equality in education

Gender-equitable education systems empower girls and boys and promote the development of life skills – like self-management, communication, negotiation and critical thinking – that young people need to succeed. They close skills gaps that perpetuate pay gaps, and build prosperity for entire countries.

Gender-equitable education systems can contribute to reductions in school-related gender-based violence and harmful practices, including child marriage and female genital mutilation .

Gender-equitable education systems help keep both girls and boys in school, building prosperity for entire countries.

An education free of negative gender norms has direct benefits for boys, too. In many countries, norms around masculinity can fuel disengagement from school, child labour, gang violence and recruitment into armed groups. The need or desire to earn an income also causes boys to drop out of secondary school, as many of them believe the curriculum is not relevant to work opportunities.

UNICEF’s work to promote girls’ education

UNICEF works with communities, Governments and partners to remove barriers to girls’ education and promote gender equality in education – even in the most challenging settings.

Because investing in girls’ secondary education is one of the most transformative development strategies, we prioritize efforts that enable all girls to complete secondary education and develop the knowledge and skills they need for life and work.

This will only be achieved when the most disadvantaged girls are supported to enter and complete pre-primary and primary education. Our work:

• Tackles discriminatory gender norms and harmful practices that deny girls access to school and quality learning.
• Supports Governments to ensure that budgets are gender-responsive and that national education plans and policies prioritize gender equality.
• Helps schools and Governments use assessment data to eliminate gender gaps in learning.
• Promotes social protection measures, including cash transfers, to improve girls’ transition to and retention in secondary school.
• Focuses teacher training and professional development on gender-responsive pedagogies.
• Removes gender stereotypes from learning materials.
• Addresses other obstacles, like distance-related barriers to education, re-entry policies for young mothers, and menstrual hygiene management in schools.

More from UNICEF

1 in 3 adolescent girls from the poorest households has never been to school

Let’s shape tech to be transformative

Gender-responsive digital pedagogies: A guide for educators

Stories of suffering and hope: Afghanistan and Pakistan

Catherine Russell reflects on her first field visit as UNICEF's Executive Director

Where are the girls and why it matters as schools reopen?

School closures due to the COVID-19 pandemic risk reversing the massive gains to girls’ education

Advancing Girls' Education and Gender Equality through Digital Learning

This brief note highlights how UNICEF will advance inclusive and transformative digital technology to enhance girls’ learning and skills development for work and life.

Reimagining Girls' Education: Solutions to Keep Girls Learning in Emergencies

This resource presents an empirical overview of what works to support learning outcomes for girls in emergencies.

e-Toolkit on Gender Equality in Education

This course aims to strengthen the capacity of UNICEF's education staff globally in gender equality applied to education programming.

Fixing the Broken Promise of Education for All

This report draws on national studies to examine why millions of children continue to be denied the fundamental right to primary education.

GirlForce: Skills, Education and Training for Girls Now

This report discusses persistent barriers girls face in the transition from education to the workforce, and how gender gaps in employment outcomes persist despite girls’ gains in education.

UNICEF Gender Action Plan (2022-2025)

This plan specifies how UNICEF will promote gender equality across the organization’s work, in alignment with the UNICEF Strategic Plan.

Global Partnership for Education

This partnership site provides data and programming results for the only global fund solely dedicated to education in developing countries.

United Nations Girls’ Education Initiative

UNGEI promotes girls’ education and gender equality through policy advocacy and support to Governments and other development actors.

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Article Contents

1. introduction, 4. discussion, 6. conclusion.

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What We Learn about Girls’ Education from Interventions That Do Not Focus on Girls

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David K Evans, Fei Yuan, What We Learn about Girls’ Education from Interventions That Do Not Focus on Girls, The World Bank Economic Review , Volume 36, Issue 1, February 2022, Pages 244–267, https://doi.org/10.1093/wber/lhab007

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What is the best way to improve access and learning outcomes for girls? This review brings together evidence from 267 educational interventions in 54 low- and middle-income countries – regardless of whether the interventions specifically target girls – and identifies their impacts on girls. To improve access and learning, general interventions deliver average gains for girls that are comparable to girl-targeted interventions. General interventions have similar impacts for girls as for boys. Taken together, these findings suggest that many educational gains for girls may be achieved through nontargeted programs. Many of the most effective interventions to improve access for girls relax household-level constraints (such as cash transfer programs), and many of the most effective interventions to improve learning for girls involve improving the pedagogy of teachers. Girl-targeted interventions may make the most sense when addressing constraints that are unique to, or most pronounced for, girls.

Investing in girls’ education has been called “the world's best investment” ( Sperling and Winthrop 2015 ). But how can policymakers do so most effectively? Evidence on what works to improve the quality of education is accumulating at an unprecedented rate ( World Bank 2018b ). 1 In recent years, hundreds of impact evaluations in low- and middle-income countries have demonstrated the effectiveness – or lack thereof – of a range of interventions at improving education outcomes, for girls and boys ( Evans and Popova 2016 ; J-PAL 2017 ). Reviews that examine the most effective ways to boost girls’ education tend to focus on interventions that target girls – for example, building girls’ latrines at schools or providing scholarships for girls – potentially missing large educational benefits for girls from interventions that are not gender-specific ( Unterhalter et al. 2014 ; Sperling and Winthrop 2015 ; Haberland, McCarthy, and Brady 2018 ). 2

This paper reports the results of a systematic review identifying the most effective interventions to improve girls’ access to education and learning outcomes within an evidence base that includes both girl-targeted and general education interventions. The study poses three research questions: (1) Are girl-targeted interventions more effective for girls’ outcomes than general interventions? (2) For general, non-targeted interventions, do impacts on girls tend to be larger? and (3) In absolute terms, what are the most effective interventions for girls?

To answer these questions, the study collected and examined a large database of education studies with access or learning outcomes for students. It categorized the studies as either evaluating girl-targeted or non-targeted (i.e., general) interventions and identified all studies that reported gender-differentiated impacts. Only one in three studies of interventions not targeted to girls report disaggregated impacts by gender, so a first implication of this work is that in order to understand how best to improve girls' education, studies should consistently report impacts for girls. For those studies that did not report gender-differentiated impacts, the study contacted their authors asking them either to run the additional gender-differentiated analysis or to share the data. The effects of different programs were then standardized to increase comparability of effect sizes across studies. Ultimately, the effects for girls from 175 studies were synthesized. (The full list of studies is available in S1 of the supplementary online appendix .)

The study finds that general, nontargeted interventions perform similarly to girl-targeted interventions on average to increase both girls’ access to school and their learning in school. General interventions tend to have similar effects for girls and for boys. (The evidence suggests that if anything, girls benefit slightly more from general interventions, although the differences are not statistically significant.) In examining the most and least effective interventions for girls’ education, the study finds that girls’ access to school is more responsive to changes in costs, distance, and sanitation conditions; while girls’ learning is more likely to be improved by structured pedagogy and interventions that help teachers to teach at the right level. Later sections of the paper discuss the implications for inequality between boys and girls, cost-effectiveness of programs, and what circumstances lend themselves to general versus targeted interventions.

General, nontargeted interventions may be more politically palatable for scaling up by national governments – since constituents have both daughters and sons. 3 General interventions offer a wider array of evaluated interventions, giving policy makers a richer menu of options among nontargeted interventions to improve girls’ education. In countries where boys also struggle to achieve quality education, general interventions can simultaneously improve girls’ learning while benefitting boys as well. None of this suggests that programs will not benefit from considering gender issues in their design. For programs with a per-pupil expenditure (like cash transfers or scholarships), targeted versions will be significantly less costly for the simple reason that they will only need to budget for the cost of transfers or scholarships to girls (and not to boys). Some investments in quality, like pedagogical programs delivered through teachers, may be less sensitive to the number of beneficiary students.

This analysis is subject to certain limitations. First, there are far more general interventions than girl-targeted interventions. Second, not all general studies report gender-disaggregated results, although those results were obtained from the authors whenever possible, including for many studies that did not initially report them. Third, this study focuses on access to school and on learning outcomes, whereas some girl-targeted interventions may focus on other outcomes. Fourth, many of the interventions included in this review focus on primary education, and as girls reach adolescence, they may face more gender-specific constraints.

Bearing those caveats in mind, these results suggest that to achieve access and quality, especially in primary education, specifically targeting girls may not always be necessary to help those girls succeed. If policy makers want to help girls learn, one strategy will be to make schools better for all children.

The project gathered a large collection of studies that report education outcomes, either access or learning. For each of the studies, the project identified whether or not they separately report impacts for boys and girls. For studies that separately report impacts for boys and girls, this project extracted those data, standardized the estimates, and used them to compare the impacts for boys versus girls and across programs for girls. For studies that do not separately report, the researchers contacted the authors and asked them either to share the data or to provide the separate estimates themselves. This section reports on each step in detail.

Literature Search

The study began with a large database of education impact evaluations compiled for Evans and Popova (2016) and subsequently updated it. The database consists of 495 studies that were cited in 10 recent systematic reviews of evidence on what works to improve learning and access in low- and middle-income countries. 4 All the reviews were published or made publicly available between 2013 and 2015, and the studies included were conducted between 1980 and 2015. Another systematic review of interventions with a special focus on access outcomes came out in 2017 ( J-PAL 2017 ); its references added four studies to the database.

To increase the coverage of studies that were published (either as working papers or peer-reviewed articles) after 2015, the researchers conducted an additional literature search between October 2017 and January 2018. They searched Google Scholar and the websites of major institutions that conduct research related to low- and middle-income countries for working papers or research reports that were published between 2015 and 2017 containing the keywords “evidence,” “education,” “access,” “learning,” “enrollment,” “dropout,” “attendance,” or “score.” The same search terms were applied to several economics and education journals, which are listed in S2 of the supplementary online appendix . These two additional searches yielded 19 new studies. In total, 518 papers were reviewed.

Inclusion Criteria

The project included studies of education interventions (such as teacher professional development and providing textbooks), health interventions (such as providing deworming drugs and micronutrients), and safety net interventions (such as cash transfers). The present study only included studies that took place in preprimary, primary, and secondary schools in low- or middle-income countries, according to the World Bank definition ( World Bank 2020 ). To be included, studies had to be published – either as a working paper or a journal article – between 1980 and 2017 and had to report at least one of the following education outcomes: access outcomes (enrollment, dropout, or attendance) or learning outcomes (composite test score or any subject score). Nonacademic skill development programs for adolescents were not included.

The project only included studies that used an experimental or quasi-experimental design. To be included, studies needed to have a valid counterfactual – in other words, a credible way of determining what would have happened in the absence of the program. The ways that studies could construct such a counterfactual included random assignment of treatment, difference-in-differences analysis, regression discontinuity, instrumental variables, and propensity score matching. At the same time, the analysis was restricted to studies where girls are included in the intervention group.

Data Collection

Upon reviewing the 518 identified studies, 328 studies met the inclusion criteria. These studies were further divided into two groups: girl-targeted interventions and general interventions. Girl-targeted interventions include any intervention that is explicitly designed to boost education outcomes for girls specifically. For example, this includes programs that provide girls with cash or in-kind transfers, reduce tuition or other school costs for girls, offer merit scholarships to girls, build latrines for girls in schools, reduce travel distance to schools for girls by building village schools or providing transportation, provide female teachers, or implement girls’ empowerment curricula in schools. In general, if the program either specifically targets girls for benefits or explicitly states its objective as improving girls’ educational outcomes, it is counted as “girl-targeted.” The researchers identified 18 studies designed to increase access or learning specifically for girls. The other 310 studies were general interventions. 5

“General interventions” refer to programs that are gender neutral in their design. Examples include programs that offer computer-assisted learning for all students, provide school meals for all students, and distribute free school uniforms or textbooks to all students. A general intervention may disproportionately benefit girl students, but it is not explicitly designed to do so, nor is it targeted specifically to girls.

To collect the impacts of interventions on girls for the 18 girl-targeted studies, the project used the results on girls reported in the studies. For general interventions, the average effect reported in the study covered an average across boys and girls, so the researchers verified which studies also reported effects separately: 105 studies reported heterogeneous intervention impacts by gender in their original papers, and those results were incorporated in the review. However, that left 205 studies that did not report gender-differentiated impacts in their original papers. The authors of these studies were contacted up to three times between January 2018 and July 2018, with the request that they either provide additional estimates of intervention effects by gender or share the data of their studies with the project to perform the analysis on their behalf. Authors were given at least three months to reply with either new estimates or their data if they were interested.

Of the 205 studies, the project received replies from the authors of 104 studies. Among them, the authors of 32 studies indicated that the data were no longer available or that gender data were not collected. Another 72 sets of authors expressed their willingness to run the additional analysis (50 papers) or share their data (22 papers). By the end of July 2018, it was possible to obtain new estimates of effects by gender of 52 studies.

Figure 1 displays the review process. Combining girl-targeted interventions, general interventions that report impacts on girls, and the new estimates the project collected from authors, the final sample of this review consists of 175 studies evaluating 267 total interventions. Among those studies, 86 measured access outcomes such as enrollment, attendance, or dropout; and 118 measured learning outcomes including a composite test score, math score, or language score ( table 1 ).

Review Method

Source : Authors’ calculations.

Descriptive Statistics of the Overall Sample

Note : Access and learning do not sum to the total because multiple studies report both learning and access outcomes.

Coding of Effect Sizes

In this paper, the unit of analysis is the estimated impact of an intervention, where a group that received an intervention is compared to another group that did not receive the intervention. For studies with multiple treatment arms, the project coded the impact of each treatment arm separately (as its own intervention) and recorded the education outcomes corresponding to that intervention. For example, Berlinski et al. (2016) tested the effects of four interventions or treatment arms: (1) an active learning approach to the teaching of math, (2) an active learning approach plus an interactive white board, (3) an active learning approach plus a computer lab, and (4) an active learning approach plus one computer per student. The project coded these four experiments as four separate interventions. Furthermore, if studies reported multiple estimates for a given intervention, the project coded all of those estimates separately rather than creating a composite variable.

Qualitative Variables

A set of additional variables was collected to better characterize the most effective interventions for girls. The variables included country, region, implementation agency, location (rural or urban), intervention level (village, school, household, or individual), duration of intervention (single contact or repeated contact), number of intervention components (single or multiple), the level of education at which the intervention was implemented (preprimary, primary, or secondary), student age, major program components (such as reducing school costs, a health intervention, additional teaching and learning materials, or school grants), the presence of components identified by program implementers as “girl friendly,” cost data (if any), quality of the outcome data (e.g., administrative data, self-reported data, national tests, international tests, program designed tests). For each study, its publication type and evaluation design were also coded.

Where and What Are the Girl-Targeted Interventions?

Girl-targeted interventions are distributed differently from general interventions, both in terms of their characteristics and where they are placed ( table 2 ). Girl-targeted interventions are more likely to cover both primary and secondary than general interventions. Girl-targeted and general interventions have a similar urban/rural distribution, but the smaller sample of girl-targeted interventions means that almost all are in either rural areas or both urban and rural areas. Girl-targeted and general interventions are, similarly, likely to have a single component or multiple components. Studies of girl-targeted interventions are slightly more likely to be published as journal articles (72 percent) rather than working papers, as opposed to general interventions (64 percent).

Descriptive Statistics of Included Interventions

The starkest difference between the samples is that almost all of the girl-targeted interventions are located in either South Asia (52 percent) or Sub-Saharan Africa (45 percent). While there are many general interventions from those regions, there are also many general interventions in Latin America and the Caribbean. This difference is unsurprising, given that the gender gap has flipped in Latin America and the Caribbean, with girls tending to outperform boys, whereas South Asia and Sub-Saharan Africa remain the regions with the widest gender gaps favoring boys ( Evans, Akmal, and Jakiela 2021 ).

Girl-targeted interventions are also concentrated in a subset of classes of interventions ( table 3 ). Most girl-targeted interventions fall into three categories: individual transfers – that is, cash transfers, in-kind transfers, or scholarships (35 percent), school infrastructure (28 percent), or girls’ empowerment programs (21 percent). The latter group includes gender awareness education, marriage delay incentives and skill development, and so forth. General interventions fall in some of the same categories (e.g., 29 percent are individual transfers) but also in others: 29 percent involve some sort of teacher-focused intervention (such as professional development, improved pedagogy, or teacher incentives). The study's results include analysis of subgroups where there is a significant concentration of both girl-targeted and general interventions.

Categories of Included Interventions

Finally, the study examines the association between whether an intervention is girl-targeted and certain additional characteristics (table S3.1 in the supplementary online appendix ). Interventions implemented by nongovernment organizations are 11.7 percentage points more likely to be targeted to girls. Secondary school programs are also more likely to be targeted to girls. Girl-targeted interventions are slightly more common in countries with lower girls’ primary school completion and enrollment rates, lower performance of girls on the human capital index, and in countries with higher adolescent marriage and adolescent childbirth rates. These associations may not be causal, but they are suggestive, both that girl-targeted interventions are more common among NGOs and in places where girls face more obstacles.

Are Girl-Targeted Interventions the Most Effective for girls?

In terms of increasing girls’ participation in school, girl-targeted interventions and general interventions perform similarly on average, although there are some girl-targeted interventions that outstrip general interventions.  Figure 2 demonstrates the result of a random effects meta-analysis comparing the effect sizes for schooling access for both general interventions and girl-targeted interventions. The average effect size is larger for girl-targeted interventions (0.15 SDs) than for general interventions (0.11 SDs), but the differences are not statistically significant. (Significance is indicated with asterisks in  fig. 2 and reported explicitly in table S3.2 in the supplementary online appendix .)

Effect Sizes of Access Outcomes for Girls

Note : A random-effects model was used to estimate the meta results for each group of studies. Error bars report the 95 percent confidence interval for the estimates. Country groups followed the World Bank country group definitions ( World Bank 2020 ). * Difference in effect sizes between general studies and girl-targeted studies is significant at the 0.05 level, and p -values are reported in table S3.2 in the supplementary online appendix .

In South Asia, where there is a significant sample of both general interventions and girl-targeted interventions with access outcomes, the point estimates are very similar: 0.08 SDs for girl-targeted interventions and 0.09 SDs for general interventions. In Sub-Saharan Africa, girl-targeted interventions have a larger average effect (0.23 SDs versus 0.09 SDs for general interventions), but the confidence interval for girl-targeted interventions is enormous, from 0.01 to 0.45 SDs, suggesting wide variation in performance.

When examining specific classes of programs for which several girl-targeted interventions are available, similar point estimates are seen for school infrastructure (0.10 SDs for girl-targeted interventions and 0.12 SDs for general interventions). For individual transfers, larger point estimates are seen for girl-targeted interventions (0.23 SDs) than for general interventions (0.15 SDs), but the confidence interval for girl-targeted interventions is – like the estimates for Sub-Saharan Africa – very wide, suggesting wide variation in performance of these programs. For studies that took place in rural areas, general studies have larger point estimates (0.15 SDs) than girl-targeted studies (0.06 SDs), and the difference is statistically significant ( fig. 2 and table S3.2 in the supplementary online appendix ). The study likewise does not see any clear differences for programs in primary school or secondary school only. In an ideal scenario, one might control for multiple characteristics, but the relatively small sample of girl-targeted interventions examined in this study makes that econometrically infeasible.

An alternative way to compare these interventions is to examine the distribution of effect sizes for girl-targeted interventions and for general interventions – that is, list all the estimates for targeted and for general interventions separately and compare the two distributions (table S3.3 in the supplementary online appendix ). As with the meta-analytic results, similar distributions of effect sizes are found for the two sets of interventions. The median effect size for these two categories is very similar, increasing girls’ enrollment or attendance by 0.07–0.09 standard deviations. The effect sizes of less effective interventions – at the 10 th and 25 th percentiles – are also similar. However, the girl-targeted interventions at the 90 th percentile have effect sizes that are 0.07 standard deviations larger than those of general interventions. That said, there are also general interventions with large effect sizes. The effect size of the most effective general intervention (a conditional cash transfer in South Africa, Eyal, Woolard, and Burns (2014) ) –1.66 standard deviations – is comparable in size to that of the most effective girl-targeted intervention (a conditional cash transfer to girls in Malawi, Baird et al. (2016) ), at 1.54 standard deviations. None of these differences are statistically significant at standard levels (table S3.4 in the supplementary online appendix ).

There are also far more evaluated general interventions than girl-targeted interventions. As seen in  table 1 , the number of general interventions is more than three times that of girl-targeted interventions. This means that in each of the effect size bins, general interventions provide a larger menu for tested options ( fig. 3 ). Even among the most effective interventions, there are almost as many general interventions with large effect sizes (greater than 0.4 standard deviations) because so many more general interventions have been tested. Therefore, general interventions –together with girl-targeted interventions – constitute an important source of ways to improve girls’ access to education.

Number of Access Outcomes by Effect Size

Note : Effect sizes are in the unit of standard deviations.

For learning, smaller point estimates are observed for girl-targeted interventions (0.11 SDs versus 0.18 SDs for general interventions), and the difference is statistically significant at the 5 percent level ( fig. 4 ). In this case, girl-targeted interventions have – on average – similar or smaller effects as compared to general interventions in every category: primary only, secondary only, South Asia, Sub-Saharan Africa, rural, transfer programs, and infrastructure programs. Some of those subgroup differences are statistically significantly different (table S3.2 in the supplementary online appendix ): in both South Asia and Sub-Saharan Africa, effect sizes for general studies are around 0.10 SDs bigger than those for girl-targeted studies. 7

Effect Sizes of Learning Outcomes for Girls

Note : A random-effects model was used to estimate the meta results for each group of studies. The error bars show the 95 percent confidence interval for the estimates. The error bars for girl-targeted outcomes in the primary only group are not included due to values larger than the display, but they are reported to the right of the figure. Country groups followed the World Bank country group definitions ( World Bank 2020 ). *Difference in effect sizes between general studies and girl-targeted studies is significant at the 0.05 level, and exact p -values are reported in table S3.2 in the supplementary online appendix .

Comparing the distribution of effect sizes, girl-targeted and general interventions have comparable impacts on girls’ learning (table S3.3 in the supplementary online appendix ). The median interventions increase learning by 0.12 and 0.11 standard deviations. The top programs (90 th percentile) among general interventions have slightly bigger measured effect sizes (0.52 standard deviations) than the top programs among girl-targeted interventions (0.33 standard deviations), although the differences are not statistically significant (table S3.4 in the supplementary online appendix ). 8

However, as with the access studies, the difference in the number of general interventions and girl-targeted interventions is significant ( fig. 5 ). This is even more the case in learning outcomes: there are 178 general learning interventions (from 106 studies) compared to only 14 girl-targeted learning interventions ( table 1 ). With just 15 girl-targeted interventions (from 12 studies), the distribution of effect sizes might be affected by outliers: in fact, the large effect size of the top girl-targeted intervention (at the 90 th percentile) is purely driven by a school construction intervention ( Kazianga et al. 2013 ), which was designed specifically to increase girls’ access to schools. 9 When schools were built in villages in rural Burkina Faso, learning outcomes for girls dramatically improved. But taking out this intervention, the effect size of girl-targeted interventions at the 90 th percentile drops to 0.10 standard deviations. Alternatively, if one drops the largest two interventions from the general interventions, the effect size changes hardly at all.

Number of Learning Outcomes by Effect Size

These findings have two potential implications. The first is that while general and girl-targeted interventions perform similarly on average, there are more proven general interventions that deliver high impacts for girls’ learning than there are girl-targeted interventions. As a result, policy makers have more options to draw from among the general interventions. The second is that insofar as governments and other actors are experimenting with innovative girl-targeted interventions, there may be value in evaluating these to build the evidence base.

For General Interventions, Do Impacts on Girls Tend to Be Larger?

Previous research shows that the demand for girls’ schooling tends to be more responsive than that for boys’ to gender-neutral education policies ( Glick 2008 ; J-PAL 2017 ). Similar point estimates are observed for girls and boys for both access and learning outcomes ( fig. 6 ). 10 Point estimates are slightly higher for girls so that – if anything – general interventions seem to be slightly more effective for girls than for boys, confirming previous work. Similar patterns are found when comparing the distribution of effect sizes (table S3.5 in the supplementary online appendix ), with no significant differences between the distributions (table S3.4 in the supplementary online appendix ). 11

Effect Sizes for Girls and Boys (General Interventions Only)

Note : A random-effects model was used to estimate the meta results for each group of studies. The error bars suggest the 95 percent confidence interval for the estimates.

What are the Most Effective Interventions for Girls?

To summarize the most effective interventions for girls, this section presents the 10 access and learning interventions with the largest effect sizes and seeks to understand their attributes. These are contrasted with the 10 least effective interventions in terms of access and learning outcomes. The effect sizes from all studies underlying the analysis are included in S4 in the supplementary online appendix . An alternative approach would be to carry out a formal meta-analysis: As the results demonstrate, there is a great deal of variation within categories of interventions (such as cash transfers), such that taking the average effect of a category is unlikely to yield meaningful insights. 12 Because the study codes each estimate of the impact of an intervention separately, the fact that 1 estimate appears in the 10 most effective or least effective does not mean that all estimates of the impact of that intervention are among the most or least effective.

The 10 studies that report the largest impacts in improving access to education for girls report greatly improved girls’ participation in school, with an average effect size of 0.73 standard deviation ( table 4 ). Three of the 10 are girl-targeted interventions, including cash transfers to girls who had previously dropped out of school – conditional on school attendance in Malawi ( Baird et al. 2016 ), improving school water and sanitation systems in Kenya ( Garn et al. 2013 ), and providing private school subsidies for girls in Pakistan ( Kim, Alderman, and Orazem 1999 ). Six of the general interventions are similarly related to offering cash for education in different countries ( Maluccio, Murphy, and Regalia 2010 ; Edmonds and Shrestha 2014 ; Eyal, Woolard, and Burns 2014 ; Benhassine et al. 2015 ; Duflo, Dupas, and Kremer 2017 ), building village schools in Afghanistan ( Burde and Linden 2013 ) and another intervention is focused on malaria prevention in The Gambia ( Jukes et al. 2006 ). Altogether, 6 of the 10 involve cash transfers, and one more – subsidies in Pakistan – similarly involves reducing the cost of schooling.

The 10 Most Effective Interventions to Improve Access to Education for Girls

Source : Authors’ calculations based on cited works.

*Girl-targeted interventions.

These top interventions demonstrate that reducing the cost of schooling is likely the single most effective way to bring girls into school. Most of these are conditional cash transfers, although fewer unconditional transfers have been tested. In addition, reducing indirect costs – such as the commuting distance to school for girls by building village schools – has been effective in increasing access. Note, however, that one unconditional cash transfer – without a schooling condition – is among the less effective interventions ( table 5 ) ( Baird, McIntosh, and Özler 2011 ). Improving health conditions through either better sanitation facilities or controlling malaria tends to attract more girls to school as well.

The 10 Least Effective Interventions to Improve Access to Education for Girls

1 Adjusted negative value for comparison.

There are concerns about the effectiveness of conditional cash transfer programs if only considering the most effective interventions. One of them is that the popularity of conditional cash transfers has led to an emergence of impact evaluations in this field, which might lead this class of interventions to be overrepresented in the evidence base. Cash transfer interventions could be among both the most effective and the least effective interventions. To test this, this study summarizes the bottom 10 interventions to increase access for girls in  table 5 . There are three transfer programs – conditional, cash, or in-kind – that were particularly ineffective in bringing girls into school, such as those in Burkina Faso ( Kazianga, De Walque, and Alderman 2012 ), the Philippines ( Chaudhury, Friedman, and Onishi 2013 ), and Uruguay ( Amarante, Ferrando, and Vigorito 2013 ), but transfer programs represent far more of the most effective than the least effective programs. There is more variation in the least effective programs, ranging from providing school meals to targeted savings accounts for education. Interestingly, it is seen that within the same study ( Garn et al. 2013 ), while promoting hygiene, improving water treatment, improved sanitation, and safe water storage in Kenyan primary schools is one of the best ways to increase girls’ enrollment, promoting hygiene and improving water storage alone actually reduced enrollment for girls. Although it is likely that girls are more responsive to sanitation conditions, different environments face different challenges: in Nepal providing sanitary products did not increase girls’ school attendance, likely in part because very few girls reported missing school due to a lack of sanitary products ( Oster and Thornton 2011 ).

For learning, the average effect size of the top interventions for girls is 0.98 standard deviation ( table 6 ). Compared to access interventions, there is more variation in the design of learning interventions. First, only 2 in 10 studies are girl-targeted interventions. One of the two girl-targeted interventions is a public-private partnership initiative in schools in Pakistan providing a gender-differentiated subsidy that increased girls’ test score by 0.77 standard deviation. The other intervention arm in the same initiative provided a gender-neutral subsidy and also yielded sizeable effects, albeit smaller than the gender-differentiated one ( Barrera-Osorio et al. 2017 ). The other top-10 girl-targeted intervention is the Afghan village school program for girls that delivered significant impacts on girls’ access and learning outcomes ( Burde and Linden 2013 ). A general (nontargeted) community school program in Honduras greatly improved girls’ math score ( Di Gropello and Marshall 2011 ).

The 10 Most Effective Interventions to Improve Learning for Girls

1 TaRL: Teaching at the Right Level.

Several of the most effective general interventions for girls among the top 10 involve structured pedagogy in early grades, or providing teachers with clear guidance on teaching or even scripted lesson plans. These interventions have been shown to be highly effective in several Sub-Saharan African countries including South Africa, Liberia, and Kenya ( Piper 2009 ; Piper and Korda 2010 ; Piper and Mugenda 2014 ; Piper, Zuilkowski, and Ong'ele 2016 ). Another category of interventions that work well for girls (and boys) are those that help teachers to teach children at their current level of learning (e.g., teaching at the right level), either through diagnostic feedback or software as reported in Banerjee et al. (2016) and Imbrogno (2014) .

On the other hand, the least effective programs for girls’ learning are all general interventions ( table 7 ). Various interventions actually had negative impacts on learning for girls compared to “schooling-as-usual,” but often, those same programs did not work for boys either. For example, technology interventions – whether substituting teachers with computers or providing students with laptops – did not help improve learning ( Linden 2008 ; Sharma 2014 ). Although there are teacher professional development programs that work to improve student learning ( Popova et al. forthcoming ), the present study's findings demonstrate that introducing new pedagogical methods through a short teacher training program is less likely to be effective to improve girls’ learning; and this is true no matter which education level the intervention targets ( Yoshikawa et al. 2015 ; Berlinski and Busso 2017 ). In addition, school accountability interventions such as distributing school report cards to students and parents were not effective for girls in Sri Lanka ( Piper and Korda 2010 ; Aturupane et al. 2014 ).

The 10 Least Effective Interventions to Improve Learning for Girls

Up until this point, this paper has focused on identifying the interventions that deliver the highest absolute learning gains for girls. An alternative approach would be to identify those programs that benefit girls most relative to boys. In other words, this approach would focus on closing inequalities (or increasing them, in contexts where girls are ahead in school) rather than merely improving girls’ access and learning without regard to boys’ performance.  Figure 7 shows the gains in access and learning for boys versus those for girls. The programs with the most unequal impacts – both favoring girls and favoring boys – are general interventions. Almost all of the girl-targeted interventions for which data are available on both girls and boys have similar results for both genders, with slightly better results for girls: 12 in 18 girl-targeted studies do not report outcomes for boys. If one's objective were purely inequality reduction, then cash transfers in South Africa had dramatically larger access impacts on girls than on boys, despite not being gender-targeted ( Eyal et al. 2014 ). A mother tongue learning instruction in Kenya in the Lubukusu language had no discernible impact on boys’ learning but a sizeable impact for girls ( Piper, Zuilkowski, and Ong'ele 2016 ). However, there are no clear patterns as to which classes of interventions are inequality-enhancing versus inequality-reducing. For this inequality analysis, the project drops girl-targeted studies that do not report impacts for boys. If one were to assume that those girl-targeted studies that did not report outcomes for boys had zero impact on boys, then girl-targeted interventions would decrease inequality more than general interventions.

Relative Effect Sizes for Boys versus Girls

Note : Grey points represent effect sizes of general interventions, black dots represent effects sizes of girl-targeted interventions, and the dashed line represents same effect sizes for girls and boys. For access outcomes, 171 effect sizes of general interventions and 12 (out of 34) effect sizes of girl-targeted interventions are plotted; for learning outcomes, 423 effect sizes of general interventions and 5 (out of 20) effect sizes of girl-targeted interventions are plotted. The missing girl-targeted interventions are those that did not report outcomes for boys.

While the study standardized effect sizes across interventions in this review, incorporating cost data would enhance the analysis, as the most effective programs may not be the most cost-effective and therefore not easy to scale up. However, despite a strong demand for cost data, few studies report them. McEwan (2015) in his review stated that 56 percent of studies reported no cost details, and most of the rest reported minimal information. The present study encountered similar problems when it tried to collect cost data. In addition, even when cost data are reported, they are often not comparable due to different accounting methods. Taking an early childhood development program in rural Mexico as an example, the cost per child estimated by World Bank researchers was $76 ( Cárdenas, Evans, and Holland 2015 ), but when evaluated by another group of researchers at Brookings, the cost per child almost doubled to $174–$202 ( Gustafsson-Wright, Boggild-Jones, and Gardiner 2017 ). Ideally, a separate initiative would collect cost data following a standard set of guidelines such as those laid out in Dhaliwal et al. (2013) .

This review finds that general interventions are often comparable in impact to girl-targeted interventions in improving access to school and learning once at school. But if a policy maker's primary concern is improving girls’ education, then perhaps investing in girl-targeted interventions would allow similar gains at much lower cost – that is, just paying for the girls rather than girls and boys. This argument plays out differently for access versus learning interventions. For access interventions such as cash transfers, the cost could indeed be potentially reduced by targeting only girls. Indeed, several of the most effective general interventions were cash transfer programs that happened to not target girls specifically. One could imagine replacing those programs with girl-only cash transfer programs and potentially achieving similar gains. For learning interventions, such as structured pedagogy interventions, many are introduced at the level of the school, so that in mixed-gender schools, there is no clear cost gain to trying to limit the impact to girls only.

Program Attributes

This study gathered data on a number of program attributes with the aim to provide more information on the most effective programs. For example, the average program size of the most effective access interventions is 262 students, and for learning interventions it is 556 students. With the exception of the cash transfer programs, all others among the 10 most and least effective programs are pilot programs. This is a result of the fact that most interventions that are carefully evaluated tend to be pilots. Therefore, it is not possible from this sample to infer whether or not pilot programs are more effective than those that have been implemented at scale. Another attribute that was examined was the level of education that the top programs targeted. In terms of access interventions, 7 of the top 10 interventions targeted school-aged children in general, often between age 6 up to age 16 – working through the household rather than the school, trying to get out-of-school children into school. For learning interventions, 9 in 10 focused on the primary level, and half of them were designed to improve learning in grades 1–3. There is great interest in programs for adolescent girls, but many of those programs focus on building life skills and increasing earning capacity directly (see, for example, Adoho et al. 2014 ; Bandiera et al. 2018 ; Bandiera et al. 2019 ) rather than keeping girls in school and increasing their learning ability. In many low- and middle-income countries, children and youth can still significantly improve their literacy and numeracy all through primary and secondary school ( Evans and Yuan 2019 ), and so there will be great value in continuing to evaluate programs and increase learning and access for adolescents. The study also examined if authors included any gender component in their interventions. It found that besides girl-targeted programs, only 1 general intervention in the top 20 had a girl-friendly component, which was to provide gender-differentiated school subsidies ( Barrera-Osorio et al. 2017 ).

What Has Been Studied

A key limitation of this work is that it only surveys those interventions that have been evaluated. One can imagine a wide array of girl-targeted interventions that could still be tried or that have been tried but not yet rigorously evaluated. In the context of strict budget constraints, having clear data on the best investments among those interventions that have been evaluated can be useful, and it can help governments and other education stakeholders to avoid investing in programs that have proven ineffective. However, it should not stop policy makers from continuing to innovate and test new programs that relax constraints on girls’ access and learning. Another, related limitation is that there are far fewer girl-targeted interventions in this study's sample. As researchers continue to test girl-targeted interventions, this evidence will continue to evolve.

When Should Programs Target Girls’ Education?

Children in low- and middle-income countries face a wide range of constraints to their education. Psaki et al. (forthcoming) group these constraints into three categories. The first category of constraints almost exclusively affects girls, such as adolescent pregnancy, child marriage, or social norms that devalue girls’ education. The second category affects both boys and girls but may disproportionately affect girls’ education because of inequitable gender norms, such as the cost of schooling or a lack of access to schools. The third category affects both boys and girls (although of course there may still be differences), such as low-quality pedagogy in schools. This study's finding that nontargeted interventions can be as effective as targeted interventions points to the fact that in many contexts, the third category of constraints may be binding. However, contexts vary, and whether the right intervention is targeted or non-targeted – or a combination of the two – depends on the collection of constraints that children face in a given setting.

Previous reviews of what works to improve girls’ education tend to focus on girl-targeted interventions. That approach omits key evidence of the impact of general education interventions on girls. This review brings together a large evidence base of general interventions that report effects for girls and collects additional estimates for a sample of studies that did not report effects for girls. Based on 175 studies from 54 countries, this review finds that girls’ access to school is more responsive to changes in costs, distance, and health conditions; while girls’ learning is more likely to be improved by structured pedagogy and interventions that help teachers to teach at the right level.

While this review focuses on girls’ education, the global learning crisis impoverishes both girls and boys ( World Bank 2018b ). This study's findings demonstrate that gender-neutral interventions hold great promise for girls’ learning as well as for boys. Considering the limited resources that education systems in most low- and middle-income countries possess, the most practical approach to help girls learn may be to make schools better for all children. Such an approach may also be more politically palatable to voters – who have sons as well as daughters – than programs that restrict their benefits to girls. At the same time, this approach comes at a cost: interventions that involve per-pupil transfers will obviously be cheaper if targeted.

Finally, attending school and acquiring learning are not the finish line for girls’ education. The ultimate objective is that girls can empower themselves through education and achieve their life aspirations. To this point, very few evaluations have included either long-term follow-ups or these broader measures of well-being. But gaining literacy and numeracy are the foundation for positive longer-term outcomes.

Specifically, fig. S4.1 in World Bank (2018b) demonstrates the rapid growth of studies that examine learning outcomes. Studies that examine access outcomes have also grown.

J-PAL (2017) looks at nontargeted programs, but focuses only on access outcomes and, within that, only on randomized controlled trials.

Those preferences may be different among international donors. Among the general, nontargeted interventions in this analysis, 43 percent were implemented by government agencies, whereas only 28 percent of girl-targeted interventions were conducted by governments. Furthermore, government-implemented interventions benefited more girls (at least, as proxied by the size of the evaluation treatment groups), with an average treatment group sample size (5,158) about four times that of interventions implemented by nongovernment organizations (NGOs) (1,276).

The 10 reviews are Kremer, Brannen, and Glennerster (2013) , Krishnaratne and White (2013) ; Glewwe, Maïga, and Zheng (2014) , Ganimian and Murnane (2016) ; McEwan (2015) , Masino and Niño-Zarazúa (2016) , Glewwe and Muralidharan (2016) ; Asim et al. (2017) , Snilstveit et al. (2017) , and Conn (2017) . Conn, Glewwe et al., McEwan, and Masino and Niño-Zarazúa only include studies with learning outcomes. The other reviews include studies with learning outcomes and studies with access outcomes. The database is available at “Database of Education Studies,” https://sites.google.com/site/davidkevans/database-of-education-studies .

There are three general intervention studies that contain a girl-targeted intervention arm, but for the purpose of counting, because the bulk of the benefits do not target girls, the project includes them in the general intervention group.

This paper, wherever applicable, collected the mean difference with controls for observable variables.

In additional analysis, the study compares both access and learning based on whether the studies are published or not. The study finds that for access outcomes, there is no statistically significant difference in the estimates between published papers and nonpublished papers. For learning outcomes, the point estimates for published general studies (0.13 SDs) are actually smaller than the estimates (0.24 SDs) for nonpublished papers, whereas published girl-targeted studies have slightly larger point estimates (0.11 SDs) than nonpublished studies (0.09 SDs).

The largest effect size for learning outcomes is a very large 2.56 SDs ( Piper 2009 ), whereas the largest for girl-targeted interventions is 0.41 SDs ( Kazianga et al. 2013 ).

The Burkina Faso program, evaluated in ( Kazianga et al. 2013 ), includes girls explicitly in the name of the program: the Burkinabe Response to Improve Girls’ Chances to Succeed.

Statistical significance is indicated with asterisks in  fig. 6 and is reported fully in table S3.2 in the supplementary online appendix .

This study also examines whether impacts on girls are larger in places with low levels of initial performance, using the harmonized learning indicators from the World Bank's Human Capital Index ( World Bank 2018a ) as well as various access indicators from the World Development Indicators ( World Bank 2017 ). No relationship is found.

Analysis of previous meta-analyses of education interventions suggests that high heterogeneity within categories limits the predictive power of meta-analysis in education ( Masset 2019 ).

David K. Evans (corresponding author) is a Senior Fellow at the Center for Global Development, Washington, DC, USA; his email address is [email protected] . Fei Yuan (corresponding author) is a doctoral student at the Harvard Graduate School of Education, Cambridge, MA, USA; her email address is [email protected] . The order of authors was determined alphabetically. The research for this article was financed by Echidna Giving, the Umbrella Facility for Gender Equality at the World Bank, and the Bill & Melinda Gates Foundation. The authors thank Eric Edmonds, Deon Filmer, Erin Ganju, Markus Goldstein, Pamela Jakiela, Oni Lusk-Stover, Mary Obelnicki, Owen Ozier, Pauline Rose, Dana Schmidt, Craig Silverstein, Lexie Wagner, Kim Wright-Violich, Louise Yorke, and various seminar audiences and anonymous referees for feedback and suggestions. The authors also thank Amina Mendez Acosta, Tara Siegel, Danielle Sobol, and Shikhty Sunny for excellent research assistance. A supplementary online appendix is available with this article at The World Bank Economic Review website.

Adoho Franck , Chakravarty Shubha Jr , Korkoyah Dala T. , Lundberg Mattias K. A. , Tasneem Afia . 2014 . “ The Impact of an Adolescent Girls Employment Program. The EPAG Project in Liberia .” Policy Research Working Paper, The World Bank . Washington, DC, USA .

Afridi Farzana . 2011 . “ The Impact of School Meals on School Participation: Evidence from Rural India .” Journal of Development Studies 47 ( 11 ): 1636 – 56 .

Google Scholar

Amarante Verónica , Ferrando Mery , Vigorito Andrea . 2013 . “ Teenage School Attendance and Cash Transfers: An Impact Evaluation of PANES .” Economía 14 : 61 – 96 .

Asim Salman , Robert S. , Amit Dar Chase , Achim Schmillen . 2017 . “ Improving Learning Outcomes in South Asia: Findings from a Decade of Impact Evaluations .” World Bank Research Observer 32 ( 1 ): 75 – 106 .

Aturupane Harsha , Glewwe Paul , Ravina Renato , Sonnadara Upul , Wisniewski Suzanne . 2014 . “ An Assessment of the Impacts of Sri Lanka's Programme for School Improvement and School Report Card Programme on Students’ Academic Progress .” Journal of Development Studies 50 : 1647 – 69 .

Baird Sarah J. , Chirwa Ephraim , De Hoop Jacobus , Özler Berk . 2016 . “ Girl Power: Cash Transfers and Adolescent Welfare. Evidence from a Cluster-Randomized Experiment in Malawi .” In African Successes, Volume 2 : Human Capital , edited by Edwards Sebastian , Johnson Simon , Weil David N. , 139 – 64 . Chicago : University of Chicago Press .

Google Preview

Baird Sarah , McIntosh Craig , Özler Berk . 2011 . “ Cash or Condition? Evidence from a Cash Transfer Experiment .” Quarterly Journal of Economics 126 (4): 1709 – 53 .

Bandiera Oriana , Buehren Niklas , Burgess Robin , Goldstein Markus , Gulesci Selim , Rasul Imran , Sulaiman Munshi . 2019 . “ Women's Empowerment in Action: Evidence from a Randomized Control Trial in Africa .” American Economic Journal: Applied Economics Forthcoming .

Bandiera Oriana , Buehren Niklas , Goldstein Markus , Rasul Imran , Smurra Andrea . 2018 . “ The Economic Lives of Young Women in the Time of Ebola: Lessons from an Empowerment Program .” Unpublished Manuscript .

Banerjee Abhijit , Banerji Rukmini , Berry James , Duflo Esther , Kannan Harini , Mukerji Shobhini , Shotland Marc , Walton Michael . 2016 . “ Mainstreaming an Effective Intervention: Evidence form Randomized Evaluations of “Teaching at the Right Level” in India .” Working Paper No. 22746 . National Bureau of Economic Research . Cambridge, MA, USA .

Barrera-Osorio Felipe , Blakeslee David S. , Hoover Matthew , Linden Leigh L. , Raju Dhushyanth , Ryan Stephen P. . 2017 . “ Delivering Education to the Underserved through a Public-Private Partnership Program in Pakistan .” Policy Research Working Paper No. 8177 . World Bank. Washington, DC, USA .

Benhassine Najy , Devoto Florencia , Duflo Esther , Dupas Pascaline , Pouliquen Victor . 2015 . “ Turning a Shove into a Nudge? A“Labeled Cash Transfer” for Education .” American Economic Journal: Economic Policy 7 (3): 86 – 125 .

Berlinski Samuel , Busso Matias , Dinkelman Taryn , Martinez Claudia . 2016 . “ Reducing Parent-School Information Gaps and Improving Education Outcomes: Evidence from High Frequency Text Messaging in Chile .” Unpublished Manuscript .

Berlinski Samuel , Busso Matias . 2017 . “ Challenges in Educational Reform: An Experiment on Active Learning in Mathematics .” Economics Letters 156 : 172 – 75 .

Borenstein Michael et al.  2009 . “ Effect Sizes for Continuous Data .” The Handbook of Research Synthesis and Meta-Analysis . 2nd ed. Edited by Cooper Harris , Hedges V. , Valentine Jeffrey C. , 221 – 35 . New York : Russell Sage Foundation .

Borkum Evan , He Fang , Linden Leigh L. . 2012 . “ The Effects of School Libraries on Language Skills: Evidence from a Randomized Controlled Trial in India .” No. w18183 . National Bureau of Economic Research .

Burde Dana , Linden Leigh L. . 2013 . “ Bringing Education to Afghan Girls: A Randomized Controlled Trial of Village-Based Schools .” American Economic Journal: Applied Economics 5 (3): 27 – 40 .

Cárdenas Sergio , Evans David K. , Holland Peter . 2015 . “ Early Childhood Benefits at Low Cost: Evidence from a Randomized Trial in Mexico .” Unpublished Manuscript .

Chaudhury Nazmul , Friedman Jed , Onishi Junko . 2013 . “ Philippines Conditional Cash Transfer Program Impact Evaluation 2012 .” World Bank Report . Manila, The Philippines .

Conn Katharine M. 2017 . “ Identifying Effective Education Interventions in Sub-Saharan Africa: A Meta-Analysis of Impact Evaluations .” Review of Educational Research 87 (5): 863 – 98 .

Dhaliwal Iqbal , Duflo Esther , Glennerster Rachel , Tulloch Caitlin . 2013 . “ Comparative Cost-Effectiveness Analysis to Inform Policy in Developing Countries: A General Framework with Applications for Education .” In Education Policy in Developing Countries . Edited by Glewwe Paul , 285 – 338 . Chicago : University of Chicago Press .

Di Gropello Emanuela , Marshall Jeffery H. . 2011 . “ Decentralization and Educational Performance: Evidence from the PROHECO Community School Program in Rural Honduras .” Education Economics 19 : 161 – 80 .

Duflo Esther , Dupas Pascaline , Kremer Michael . 2017 . “ The Impact of Free Secondary Education: Experimental Evidence from Ghana .” Working Paper. Massachusetts Institute of Technology . Cambridge, MA, USA .

Edmonds Eric V. , Shrestha Maheshwor . 2014 . “ You Get What You Pay For: Schooling Incentives and Child Labor .” Journal of Development Economics 111 : 196 – 211 .

Evans D. K. , Akmal M. , Jakiela P. . 2021 . “ Gender Gaps in Education: The Long View .” IZA Journal of Development and Migration 12 ( 1 ).

Evans David K. , Popova Anna . 2016 . “ What Really Works to Improve Learning in Developing Countries? An Analysis of Divergent Findings in Systematic Reviews .” World Bank Research Observer 31 : 242 – 70 .

Evans David K. , Yuan Fei . 2019 . “ Economic Returns to Interventions that Increase Learning .” Policy Research Working Paper. The World Bank . Washington, DC, USA .

Eyal Katherine , Woolard Ingrid , Burns Justine . 2014 . “ Cash Transfers and Teen Education: Evidence from South Africa .” School of Economics, University of Capetown. Unpublished manuscript .

Ganimian Alejandro J. , Murnane Richard J. . 2016 . “ Improving Education in Developing Countries: Lessons from Rigorous Impact Evaluations .” Review of Educational Research 86 ( 3 ): 719 – 55 .

Garn Joshua V. , Greene Leslie E. , Dreibelbis Robert , Saboori Shadi , Rheingans Richard D. , Freeman Matthew C. . 2013 . “ A Cluster-Randomized Trial Assessing the Impact of School Water, Sanitation and Hygiene Improvements on Pupil Enrolment and Gender Parity in Enrolment .” Journal of Water Sanitation and Hygiene for Development 3 : 592 – 601 .

Glewwe Paul , Maïga Eugenie W. H. . 2011 . “ The Impacts of School Management Reforms in Madagascar: Do the Impacts Vary by Teacher Type ?” Journal of Development Effectiveness 3 ( 4 ): 435 – 69 .

Glewwe P. , Maïga E. , Zheng H. 2014 . “ The Contribution of Education to Economic Growth: A Review of the Evidence, with Special Attention and an Application to Sub-Saharan Africa .” World Development 59 : 379 – 93 .

Glewwe Paul , Muralidharan Karthik . 2016 . “ Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications .” Handbook of the Economics of Education . Vol. 5 . Hanushek Eric A. , Machin Stephen J. , Woessmann Ludger , 653–743 . North Holland : Elsevier .

Glick Peter. 2008 . “ What Policies Will Reduce Gender Schooling Gaps in Developing Countries: Evidence and Interpretation .” World Development 36 : 1623 – 46 .

Gustafsson-Wright Emily , Boggild-Jones Izzy , Gardiner Sophie . 2017 . “ SECT: The Standardized Early Childhood Development Costing Tool .” Center for Universal Education at Brookings . Washington, DC, USA .

Haberland Nicole A. , McCarthy Katharine J. , Brady Martha . 2018 . “ A Systematic Review of Adolescent Girl Program Implementation in Low-and Middle-Income Countries: Evidence Gaps and Insights .” Journal of Adolescent Health 63 ( 1 ): 18 – 31 .

Imbrogno Jason. 2014 . “ Essays on the Economics of Education .” Doctoral dissertation. Carnegie Mellon University. Pittsburgh, Pennsylvania, USA .

J-PAL . 2017 . “ Roll Call: Getting Children into School .” J-PAL Policy Bulletin .

Jukes Matthew C. H. , Pinder Margaret , Grigorenko Elena L. , Smith Helen Baños , Walraven Gijs , Bariau Elisa Meier , Sternberg Robert J. , Drake Lesley J. , Milligan Paul , Cheung Yin Bun . 2006 . “ Long-Term Impact of Malaria Chemoprophylaxis on Cognitive Abilities and Educational Attainment: Follow-Up of a Controlled Trial .” PLoS Clinical Trials 1 : e19 .

Karlan Dean , Linden Leigh L. . 2014 . “ Loose Knots: Strong versus Weak Commitments to Save for Education in Uganda .” Columbia University. Working Paper 19863 . National Bureau of Economic Research . Cambridge, MA, USA .

Kazianga Harounan , De Walque Damien , Alderman Harold . 2012 . “ Educational and Child Labour Impacts of Two Food-for-Education Schemes: Evidence from a Randomised Trial in Rural Burkina Faso .” Journal of African Economies 21 ( 5 ): 723 – 60 .

Kazianga Harounan , Levy Dan , Linden Leigh L. , Sloan Matt . 2013 . “ The Effects of ‘Girl-Friendly’ Schools: Evidence from the BRIGHT School Construction Program in Burkina Faso .” American Economic Journal: Applied Economics 5 : 41 – 62 .

Kim Jooseop , Alderman Harold , Orazem Peter F . 1999 . “ Can Private School Subsidies Increase Enrollment for the Poor? The Quetta Urban Fellowship Program .” World Bank Economic Review 13 : 443 – 65 .

Kremer Michael , Brannen Conner , Glennerster Rachel . 2013 . “ The Challenge of Education and Learning in the Developing World .” Science 340 ( 6130 ): 297 – 300 .

Krishnaratne Shari , White Howard . 2013 . “ Quality Education for All Children? What Works in Education in Developing Countries .” 3ie Publications No. 0000-0 . International Initiative for Impact Evaluation (3ie) . Washington, DC, USA; New Delhi, India .

Linden Leigh L. 2008 . “ Complement or Substitute? The Effect of Technology on Student Achievement in India .” Working Paper, Columbia University .

Maluccio John A. , Murphy Alexis , Regalia Ferdinando . 2010 . “ Does Supply Matter? Initial Schooling Conditions and the Effectiveness of Conditional Cash Transfers for Grade Progression in Nicaragua .” Journal of Development Effectiveness 2 : 87 – 116 .

Masino Serena , Niño-Zarazúa Miguel . 2016 . “ What Works to Improve the Quality of Student Learning in Developing Countries? ” International Journal of Educational Development 48 : 53 – 65 .

Masset Edoardo. 2019 . “ Impossible Generalisations: Meta-Analyses of Education Interventions in International Development .” RISE Annual Conference 2019 . Washington, DC, USA . June 19–20 , 2019 .

McEwan Patrick J . 2015 . “ Improving Learning in Primary Schools of Developing Countries: A Meta-Analysis of Randomized Experiments .” Review of Educational Research 85 : 353 – 94 .

Oster Emily , Thornton Rebecca . 2011 . “ Menstruation, Sanitary Products, and School Attendance: Evidence from a Randomized Evaluation .” American Economic Journal: Applied Economics 3 : 91 – 100 .

Özler Berk , Fernald Lia C.H. , Kariger Patricia , McConnell Christin , Neuman Michelle , Fraga Eduardo . 2016 . “ Combining Preschool Teacher Training with Parenting Education: A Cluster-Randomized Controlled Trial .” Working Paper . The World Bank . Washington, DC, USA .

Piper Benjamin. 2009 . “ Integrated Education Program: Impact Study of SMRS Using Early Grade Reading Assessment in Three Provinces in South Africa .” RTI International . Research Triangle Park, NC .

Piper Benjamin , Korda Medina . 2010 . “ Early Grade Reading Assessment (EGRA) Plus: Liberia. Program Evaluation Report .” RTI International . Research Triangle Park, NC .

Piper Benjamin , Mugenda A. . 2014 . The Primary Math and Reading (PRIMR) Initiative: Endline Impact Evaluation . RTI International , Research Triangle Park, NC .

Piper Benjamin , Zuilkowski Stephanie S. , Ong'ele Salome . 2016 . “ Implementing Mother Tongue Instruction in the Real World: Results from a Medium-Scale Randomized Controlled Trial in Kenya .” Comparative Education Review 60 : 776 – 807 .

Popova Anna , Evans David K. , Breeding Mary E. , Arancibia Violeta . Forthcoming . “ Teacher Professional Development around the World: The Gap between Evidence and Practice .” World Bank Research Observer.

Psaki Stephanie , Haberland Nicole , Mensch Barbara , Chuang Erica , Woyczynski Lauren . Forthcoming . “ Policies and Interventions to Remove Gender-Related Barriers to Girls’ School Participation and Learning in Low- and Middle-Income Countries: A Systematic Review of the Evidence .” Campell Systematic Reviews .

Santana Maria Isabel . 2008 . “ An Evaluation of the Impact of South Africa's Child Support Grant on School Attendance .” Centro de Estudios Distributivos, Laborales y Sociales, Universidad Nacional de La Plata, Argentina .

Sharma Uttam. 2014 . “ Can Computers Increase Human Capital in Developing Countries? An Evaluation of Nepal's One Laptop per Child Program .” Annual Meeting of the Agricultural and Applied Economics Association, Minneapolis MN, July 27–29 .

Snilstveit Birte , Stevenson Jennifer , Phillips Daniel , Vojtkova Martina , Gallagher Emma , Schmidt Tanja , Jobse Hannah , Geelen Maisie , Pastorello Maria Grazia , Eyers John . 2017 . “ Interventions for Improving Learning Outcomes and Access to Education in Low- and Middle-Income Countries: A Systematic Review .” Campbell Systematic Reviews 13 ( 1 ): 1 – 82 .

Sperling Gene B , Winthrop Rebecca . 2015 . What Works in Girls' Education: Evidence for the World's Best Investment . Washington, DC : Brookings Institution Press .

Unterhalter Elaine , North Amy , Arnot Madeleine , Lloyd Cynthia , Moletsane Lebo , Murphy-Graham Erin , Parkes Jenny , Saito Mioko . 2014 . “ Interventions to Enhance Girls’ Education and Gender Equality .” Education Rigorous Literature Review . Department for International Development .

Visaria Sujata , Dehejia Rajeev , Chao Melody M. , Mukhopadhyay Anirban w . 2016 .“ Unintended Consequences of Rewards for Student Attendance: Results from a Field Experiment in Indian Classrooms .” Economics of Education Review 54 : 173 – 84 .

Wong Ho Lun , Luo Renfu , Linxiu Zhang , Scott Rozelle . 2013 . “ The Impact of Vouchers on Preschool Attendance and Elementary School Readiness: A Randomized Controlled Trial in Rural China .” Economics of Education Review 35 : 53 – 65 .

World Bank . 2017 . “ World Development Indicators .”

World Bank . 2018a . “ Human Capital Project .” World Bank , Washington, DC, USA .

World Bank . 2018b . “ World Development Report 2018: Learning to Realize Education's Promise .” World Bank , Washington, DC, USA .

World Bank . 2020 . “ World Bank Country and Lending Groups .” World Bank , Washington, DC, USA .

Yi Hongmei , Song Yingquan , Liu Chengfang , Huang Xiaoting , Zhang Linxiu , Bai Yunli , Ren Baoping , Shi Yaojiang , Loyalka Prashant , Chu James , Rozelle Scott . “ Giving Kids a Head Start: The Impact and Mechanisms of Early Commitment of Financial Aid on Poor Students in Rural China .” Journal of Development Economics 113 ( 2015 ): 1 – 15 .

Yoshikawa Hirokazu , Leyva Diana , Snow Catherine E. , Treviño Ernesto , Barata M. , Weiland Christina , Gomez Celia J. , Moreno Lorenzo , Rolla Andrea , D'Sa Nikhit . 2015 . “ Experimental Impacts of a Teacher Professional Development Program in Chile on Preschool Classroom Quality and Child Outcomes .” Developmental Psychology 51 : 309 – 22 .

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Advancing the Agenda in Girls' Education Research

Recommended.

Girls’ education remains a global priority for donors and many governments. In 2020, 92 percent of the FCDO’s and 77 percent of World Bank education aid money went to projects that included girls’ education. It’s been hailed as a silver bullet by prime ministers and philanthropists , and the G7 claims that “nowhere is our resolve stronger than in addressing the global set-back [post-COVID] in girls’ education.” National governments also highlight the importance of girls’ education: Liberia’s Ellen Johnson Sirleaf wrote that “investing in girls’ education is not only a moral imperative, it is a smart investment.”

This resolve, by both donors and national governments—alongside great advances in knowledge about what works to get girls in school and learning—is starting to pay dividends. Over the past two decades tremendous progress has been made to improve girls’ access to schooling. Data on learning similarly suggests that gender gaps may be narrowing . Yet, in some places, especially at the secondary level and among poor and vulnerable populations, progress remains too slow. And once women leave school, gender inequality remains acute and deeply rooted in the economic, political, and social spheres. Much remains to be done to get girls in school and learning and to better understand how education can most effectively contribute to equal adult life outcomes for men and women.

We suggest that the research community should coalesce around a set of coherent research priorities to help close gender gaps in education. In this note, we—a group of researchers who commonly adopt varying research traditions and approaches—have come together to outline some crucial next steps to advance the research agenda on girls’ education. We propose five areas where researchers can better collaborate to advance the field, and we call for better coordination among researchers—and better collaboration among researchers, policymakers, and funders—to keep advancing our knowledge and action for girls' education and women’s empowerment (Figure 1).

Figure 1. Five ways to advance girls’ education research

1. Advance knowledge of understudied gender-related barriers to education

The evidence from rigorous quantitative research on how to improve girls’ education has expanded significantly in the last few decades. A number of different approaches have proven consistently effective at improving education outcomes for girls—including addressing inadequate school access, economic barriers (such as tuition and fees, or lack of adequate food), and poor pedagogical approaches. ( Some of these interventions target girls specifically; others improve schooling overall.) For example, a 12-year evaluation of a scholarship scheme in Ghana found that adolescent girls who received the scholarship were 26 percentage points more likely to complete senior high school and 4 percentage points more likely to complete tertiary education; many of these students would not have otherwise attended school. The study also found delays in the onset of childbearing.

However, we still don’t know enough about gender-specific barriers to education. Too many girls are out of school, leaving school, or are in school but not learning. What’s going on? In many cases, we just don’t know. A recent systematic review of randomized controlled trials and quasi-experimental studies considered 18 gender-related barriers to girls’ education, from a simple lack of support for girls’ education to a lack of access to schools and a poor pedagogical environment. For eight of those barriers, there was not enough directly relevant research to draw conclusions with confidence about the impact of interventions on education outcomes. In some cases, these are interventions pursued by a great many governments and implementing organizations with the goal of advancing girls’ education, and we simply do not know enough about whether or not they are effective.

Fertile grounds for future research include child marriage and adolescent pregnancy (where just four existing studies were found), eliminating school-related gender-based violence (zero studies), health and childcare services (one study), menstrual health management (four studies), and the impact of sports programs (one study). Too few evaluations assessed whether addressing these barriers can improve education outcomes for girls. Addressing a lack of teaching materials and supplies also fell in this category of insufficient evidence because efforts to do so were all part of multicomponent interventions (and in some cases, a very minor component), and no studies were found that evaluated programs removing gender bias from textbooks or other teaching materials.

Of course, in some cases there are evaluations that assess whether addressing these barriers affects non-education outcomes, such as violence, health, or harmful gender norms. Tested interventions may be effective at achieving those goals, but we don’t know whether they are effective interventions for improving girls’ enrolment, attainment, literacy, or numeracy. Education outcomes aren’t the only reason to address gender-related barriers to schooling. Literacy and numeracy are absolutely key, but there are other reasons—rights, dignity, wellbeing—that matter. Some evaluations of menstrual health supply interventions might not be convincing for education outcomes, but if it is a priority as a matter of dignity, the question becomes which menstrual health management approaches work to achieve that goal. Eliminating some barriers, such as violence, is simply a right, and the research priority will be how schools and education systems can fulfil those rights.

Other approaches to address barriers to girls’ education (such as life skills programs) also need more research on their education impact. This is less because there are too few studies and more because the evidence has been inconsistent across programs. This is unsurprising, since the programs are often very different and the results are affected by whether the design of the program is appropriate to the context and how well the programs are implemented, their specific content, duration, etc. For these barriers, more research on implementation and how and why the programs have different effects is the priority.

Moreover, the nature and extent of barriers to girls’ education could be changing in contexts most severely affected by the effects of climate change, conflict, or health crises. This will require new evidence to understand the challenges and identify how to tackle them.

A common thread underlying all these barriers is harmful gender norms and behaviors. Many studies show that gender norms can moderate the impacts of interventions addressing health , economic , and violence  outcomes, among others. We also know that programs that directly address gender and power often have improved outcomes (including economic , sexual and reproductive health , and empowerment ), and more evidence on this in education would be valuable. Pressing questions include how to change these norms. For example, how can interventions be designed to shift harmful gender norms in a given context? How does better education for girls shift gender norms? How can schools contribute to dismantling gender norms  and behavior that perpetuate inequality, violence, and injustice? Quantitative and qualitative research can help answer these critical questions.

2. Increase evidence from understudied countries and regions

Despite the thematic gaps outlined above, the total amount of evidence on what works to help girls overcome at least some obstacles to their education has increased dramatically in recent years. But when you look closely, you find that much of that evidence comes from a relatively small number of places. Below, you can see maps of coverage from two recent reviews of evaluations to improve or expand girls’ education. While it makes sense that countries like India and China, which have large populations and diverse contexts, have more studies, large swaths of sub-Saharan Africa have little evidence about what works to improve girls’ education, and that needs to change.

Figure 2. The evidence on “what works” in girls’ education comes from relatively few locations

Source: These maps show the distribution of impact evaluation studies included in two recent reviews on girls’ education, Psaki et al. 2022 and Evans and Yuan 2022 . Note that these reviews were of available rigorous quantitative (experimental/ quasi-experimental) evaluations.

Why does this matter? We don’t need the highest quality evaluation evidence about every single program from every single context. Countries and localities can learn from what has worked elsewhere. But to take lessons from a successful program in one setting and apply them in another setting, some conditions need to be in place. For example, two conditions laid out by Bates and Glennerster are that (1) conditions in the new context are similar enough in key ways that we’d expect the same program (or some variation of it) to work, and (2) there is evidence that the program could actually be implemented well in the new context. What this means is that if the places where we have little evidence have very different conditions (e.g., students face different barriers) or very different capacity to implement programs from the places where we have relatively more evidence, then we’re going to be limited in our ability to bring what has worked to a new place. What worked in urban India may not in rural Chad.

There are many geographic gaps in our knowledge about how to improve girls’ education for which urgent research is needed, including the following:

• Low-income countries: we know much more about middle-income countries than low-income countries. While there are many low-income people in middle-income countries, the financial resources to tackle the problem and train civil servants to implement solutions at scale may be very different in low-income countries.
• Fragile and conflict-affected countries, or among refugees in other countries: Lots of challenges are magnified by the uncertainty that both systems and individuals face in fragile settings.
• Certain geographic regions, like the Sahel: No two countries have the exact same institutions and social norms (and indeed, there is often great variation within countries), so when we have scarce evidence from whole areas, we have to be very cautious about bringing evidence from elsewhere. This may be particularly important in areas where girls’ schooling or women’s labor force participation are particularly low.

We aren’t starting from zero in new locations: some solutions, like reducing the cost of admission to school and travel to school, have evidence from a wide-enough array of contexts that the key question is how best to implement them. For overcoming other challenges, as mentioned above, we lack even evidence in stable, middle-income settings, much less the most challenging environments. This needs to change.

3. Build knowledge on how interventions can be effectively implemented at scale

Much of the research on girls’ education draws conclusions from small-scale pilot interventions implemented with a few hundred or a few thousand girls, often by implementing partners such as NGOs. That creates challenges for translating these programs into scalable models that are institutionalized through government systems. Even within smaller evaluations, we sometimes don’t learn as much as we could about how girls are affected and what could scale effectively.

To see girl-focused programs implemented at scale, the research community can support policymakers by:

• Unpacking effects on girls and providing evidence on causal pathways in pilot evaluations to understand how interventions lead to behavior change and improved outcomes;
• Undertaking qualitative research alongside quantitative research to advance knowledge on complex, context-specific barriers to girls' education;
• Integrating learning into the process of scaling to effectively incorporate the lessons from piloted interventions into the program and correct along the way;
• Evaluating programs at scale to monitor the size of effects and the issues that may arise as a larger number of girls are targeted

First, unpacking the effects of all education programs for girls is critical to achieve long-term change at scale. According to a recent report, only one in three studies of education interventions (not specifically targeted at girls) disaggregate impacts by gender—an entirely fixable omission that limits what we know about improving girls’ education. Understanding gender-specific effects of education interventions may help to reveal if girls respond differently than boys to certain interventions. In addition, girl-focused interventions are often multi-component, and impact evaluations don’t always unpack which components are driving effects. For example, life skills programs often provide training for a number of different issues—from reproductive health to vocational training—and are frequently combined with savings, cash transfers, or health services. The impacts of these life skills programs are often measured on aggregate, even though scaled-up programs are often limited to fewer components. Analyzing which program components are driving impact, such as by integrating mechanism experiments into rigorous evaluations, could help us understand why and when girls respond positively to certain interventions.

Second, qualitative approaches to gender research can contribute significantly to advancing knowledge of complex, multi-layered, and contextually rooted phenomena, such as norms and culture, which often contribute to the acute and persistent constraints faced by girls. These approaches have an advantage in bringing marginalized voices and hidden perspectives to center stage and into the policy space. In doing so, qualitative research can be transformative not just for the ways in which reform is imagined and implemented by policymakers, but also for updating the understanding of the nature of the problems  themselves. Narratives grounded in empirical qualitative work can be powerful tools for influencing policy change.

Used alongside quantitative research, qualitative gender research brings complementarities . Descriptions of the subjective lived experience of girls (and boys) navigating their home and school life , the choices and negotiations they’re making, reveal processes and mechanisms that can help generate hypotheses and identify overlooked areas for intervention and reform. Qualitative research on gender is particularly suited for answering questions about the how and why, for interventions that work and those that do not.

Third, integrating learning into the scale-up process can also demonstrate whether a program can be implemented with fidelity as it grows over space and time. For example, process evaluations such as those conducted to scale the Teaching at the Right Level literacy program in Zambia can help partners identify and resolve roadblocks at each stage of implementation, setting up systems for more effective delivery over time. Conducting impact evaluations in the early stages of scale delivery may not be necessary and sometimes si mply capture implementation failu res which can be identified through simpler means.

Fourth, once programs are being delivered at scale through government systems, further impact evaluations and political economy analysis can help to assess the size of the impact and to identify what happens when many people receive the program . These may include spillover benefits to non-participants (e.g., when enough girls are going to secondary school it becomes expected for other girls as well), or it may undermine benefits (e.g., by overcrowded classrooms resulting in poor learning outcomes). This may be particularly important for policies that are less easy to implement and where there are more opportunities for impacts to be lost because of implementation challenges. Good research partnerships exist where research teams work with governments to run experimental or quasi-experimental evaluations of evidence-based, girl-focused programs as they roll out. For example, the Adolescent Girls Initiative-Kenya was piloted as a four-arm randomized controlled trial that allowed researchers and stakeholders to tease apart the program’s impactful elements (including on school retention and learning) and is now working with the Wajir County government to test a leaner version for scale that is implemented by and through the county infrastructure.

4. Get better, complementary data on girls’ education through rigorous studies, administrative data, and household surveys

Policymakers use data to make decisions about education investments from a range of sources. Three useful sources are academic studies and evaluations, household surveys, and administrative data. All have the potential to support good policymaking, especially when used in tandem with each other.

As we described above, evidence on what works to improve girls’ schooling from academic studies is accumulating fast . But, while academic studies on girls’ education are often rigorous, they tend to be small-scale and insulated from political economy challenges. In some cases, studies are run without policymaker engagement and may not answer the questions that are most important for domestic education policy decisions. The cost of policymaking without evidence is high and so researchers should take more care to ensure that their impact evaluations are relevant to policymakers. But rigorous research also needs to be complemented by other sources of data and analysis that allow diagnosis of challenges and can identify changes at scale over time.

Countries need good administrative data that provides them with timely and reliable insights. Most countries have administrative data, e.g., an education management information system (or EMIS), but in many cases they are underexploited or are of low quality, thus not serving to accurately diagnose the current challenges to girls’ education. They often lack the basic data that tells them how much education girls are getting, what quality it is, and whether they are safe at school. The lack of publicly available administrative datasets—and the inability to link those datasets to each other—prevents policymakers and researchers from answering basic questions (for example, was free secondary schooling in Ghana associated with faster enrolment by rural girls, or not?). If those administrative datasets were well cleaned and made public in a timely manner, it may still not be possible to make a causal inference on this question, but it would give policymakers a much clearer sense of whether the outcomes they seek to improve are moving in the right direction.

Beyond administrative data, which is usually collected in schools and funnelled upwards, household surveys provide insights pertaining to the welfare, wellbeing, and outcomes of children. Vitally, since these surveys are conducted in households, they include children who are not enrolled in school, illuminating key education issues like drop-out, poor literacy skills, child marriage, and gender-biased preferences in education investments. Household surveys—like the Demographic and Health Survey and the Multiple-Indicator Cluster Survey—have, for decades, generated data on the wellbeing of children and women. We encourage donors to keep funding these surveys. But we suggest that they could be even more useful for education policymakers if they included more modules directly related to education issues. For example, there is no reliable, cross-country data on violence in schools. A module on school violence in these household surveys would greatly enhance the field’s understanding, willingness, and ability to run interventions to protect girls from violence.

Alongside these three types of data and evidence, policymakers would benefit from tools to diagnose which obstacles are most acutely holding girls back. From the 18 obstacles to girls’ education that researchers have identified, what should a country prioritize? Developing a decision tree— like the one used to identify binding constraints to economic growth —that can help to identify the most pressing constraints and populating it with local data can help researchers and other advisors avoid providing policymakers with an unprioritized laundry list of problems. In addition, stronger administrative data could inform rapid and cost-effective evaluations, allowing governments to test innovations at scale in real-world settings.

5. Develop stronger partnerships within the research community and between policymakers and researchers, particularly those anchored in the Global South

There is an acute and increasingly recognized underrepresentation of rigorous research in low- and lower-middle income countries by scholars based in the Global South in economics and development scholarship. This is well documented. Chelwa found that between 2005 and 2015, only a quarter of journal articles published about Africa had at least one Africa-based author. Briggs and others found that the percentage of Africa-based authors publishing in two Africa-focused political journals has been declining over time.

This challenge extends to education research. A recent study by Mitchell, Rose and Asare analyzing the Africa Education Research Database found a large number of studies by researchers based in Africa that merit greater scholarly engagement. But they also found evidence that many international collaborations were dominated by northern researchers often driven by funder agendas (Figure 3).

Figure 3. Studies in Africa Education Research Database by country

While Southern-based researchers are the ones best placed to understand contextual education priorities and demands for research from policymakers, recognition of this has been more talk than walk. (In some sectors even talk lags!) These researchers face persistent barriers of funding and opportunity. While more attention is now being paid to research partnerships with policymakers and scholars based in the Global South, too often these still involve hiring a local field team to collect data which is shipped back to the US or Europe for analysis and then delivered to government policymakers, perhaps with a launch workshop. This often has limited policy impact, because it is not timely, locally owned, or responsive to policymaker needs.

We suggest that the girls’ education research community needs to move toward more partnerships that are anchored in countries and led by scholars in the Global South. These should be strong partnerships with teams undertaking joint research and producing analytical policy products, including engagement with government policymakers and NGOs implementing programs throughout the research process.

One example of such a research-implementation partnership is research currently underway between the University of Dar es Salaam in Tanzania, the University of Cambridge in the UK, and the NGO, Campaign for Girls’ Education (CAMFED) on how education interventions could help to improve gender norms within local communities. This highlights the benefits of collaborating on research for which there is a mutual agenda and with sustained interaction, with the aim of engaging with and informing policy and practice. Similarly, a locally grounded partnership between The Citizens Foundation (an education service provider) and researchers at the Lahore University of Management Sciences and the Institute of Development and Economic Alternatives in Pakistan is designed to generate understanding of pathways that allow women and men to leverage education to negotiate better outcomes for themselves and their families and to understand the mechanisms through which more and better quality education nudges gender norms in a positive direction.

Better coordination among researchers, policymakers, implementers, and funders will help advance knowledge and action for girls’ education

In this note, we highlight five areas that should be a focus for researchers to advance the field of girls’ education. A wealth of evidence finds girls’ education yields a wide range of benefits , for both girls and their families. Despite this, too many girls are still being left behind. We need better data and better strategies to reach those girls. And we need better evidence to change norms and promote the implementation of policies, within and outside of education systems, that will achieve gender equality beyond the school gates.

Researchers working on girls’ education need to collaborate better with each other—and with policymakers, advocates, and funders—to ensure our work is helping advance the field of girls’ education. Of course, the authors of this note are not the only researchers working on gender and education, and our analysis of priorities may not align with those of others. We welcome alternative perspectives on the best ways to move the girls’ education research agenda forward.

While education conferences abound, there are few opportunities for substantive dialogue between researchers and policymakers on these priorities. We suggest a dedicated annual meeting of education researchers, donors, and policymakers to advance the girls’ education research agenda. A meeting like this could help continue to define research priorities, give policymakers a forum to request support from researchers, and give a platform to researchers whose voices are less heard. More collaboration like this will ensure that we continue to make progress on education for girls.

Thanks to Dana Schmidt and Rachel Glennerster for their helpful comments.

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