Measuring and forecasting progress in education: what about early childhood?

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Study Justification:
The study aims to address the omission of early childhood education in measuring and forecasting progress towards Sustainable Development Goal (SDG) 4, which focuses on achieving universal access to quality early childhood development, care, and preschool education by 2030. The study justifies the importance of this inclusion by highlighting the significant brain, cognitive, and socioemotional developments that occur in early life, as well as the evidence of the large impacts of early learning on subsequent education and lifetime wellbeing.
Highlights:
1. The study provides an overview of the evidence supporting the importance of early childhood care and education.
2. It presents new analyses that demonstrate the medium- and long-term implications of early learning by examining the association between pre-primary program participation and adolescent mathematics and science test scores in 73 countries.
3. The study estimates the costs of inaction in terms of forgone lifetime earnings in 134 countries, revealing considerable losses comparable to or greater than current governmental expenditures on all education, particularly in low- and lower-middle-income countries.
4. The study concludes that prioritizing quality early childhood care and education is essential to attain SDG 4 and reduce inequalities, especially in a post-COVID era.
Recommendations:
1. Improve primary, secondary, and tertiary schooling while prioritizing quality early childhood care and education.
2. Adopt policies that support families in promoting early learning and their children’s education.
Key Role Players:
1. Government officials and policymakers responsible for education and early childhood development policies.
2. Education experts and researchers specializing in early childhood education.
3. Non-governmental organizations (NGOs) working in the field of education and child development.
4. Teachers and educators involved in early childhood education.
5. Parents and families, who play a crucial role in supporting early learning.
Cost Items for Planning Recommendations:
1. Funding for the expansion and improvement of early childhood care and education programs.
2. Investment in teacher training and professional development.
3. Resources for curriculum development and educational materials.
4. Infrastructure and facilities for early childhood education centers.
5. Research and evaluation to monitor the effectiveness of early childhood programs and interventions.
6. Support services for families, such as parental education and outreach programs.
7. Advocacy and awareness campaigns to promote the importance of early childhood education.
Please note that the cost items provided are general categories and may vary depending on the specific context and country.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The authors provide an overview of the evidence on the impacts of early childhood education and present new analyses using data from the Programme for International Student Assessments (PISA) to illustrate the associations between pre-primary program participation and adolescent test scores. They also estimate the costs of not making pre-primary programs universal. However, the abstract does not provide specific details about the methodology used in the analyses, such as the regression models or the sample size. To improve the strength of the evidence, the authors could provide more information about the statistical methods used and the representativeness of the sample. Additionally, they could include references to previous studies that support their findings.

A recent Nature article modelled within-country inequalities in primary, secondary, and tertiary education and forecast progress towards Sustainable Development Goal (SDG) targets related to education (SDG 4). However, their paper entirely overlooks inequalities in achieving Target 4.2, which aims to achieve universal access to quality early childhood development, care and preschool education by 2030. This is an important omission because of the substantial brain, cognitive and socioemotional developments that occur in early life and because of increasing evidence of early-life learning’s large impacts on subsequent education and lifetime wellbeing. We provide an overview of this evidence and use new analyses to illustrate medium- and long-term implications of early learning, first by presenting associations between pre-primary programme participation and adolescent mathematics and science test scores in 73 countries and secondly, by estimating the costs of inaction (not making pre-primary programmes universal) in terms of forgone lifetime earnings in 134 countries. We find considerable losses, comparable to or greater than current governmental expenditures on all education (as percentages of GDP), particularly in low- and lower-middle-income countries. In addition to improving primary, secondary and tertiary schooling, we conclude that to attain SDG 4 and reduce inequalities in a post-COVID era, it is essential to prioritize quality early childhood care and education, including adopting policies that support families to promote early learning and their children’s education.

We used data for 430,264 adolescents in 73 middle- and high-income countries surveyed in the 2018 Programme for International Student Assessments (PISA). The PISA is an international programme to assess adolescents’ reading, mathematics, and science literacy every 3 years for nationally representative samples of 15-year-old students enroled in school. The PISA also collects data on the characteristics of adolescents and their backgrounds. Among such information, the PISA asks students to retrospectively report how many years of pre-primary education they attended, following the International Standard Classification of Education Level 0 (ISCED-0). Using PISA data, we used multivariate regression models to assess the association between years of pre-primary education attendance and mathematics and science test scores. Given that PISA employs an imputation methodology to provide plausible values for each student’s test score, as a first step, we averaged such plausible values to obtain a single mathematics and science test score, which we standardized to have a mean of zero and standard deviation of one to aid interpretability. Subsequently, we estimated two models to test the association between students’ mathematics and science standardized test scores (zTestScorei) and binary variables indicating whether students attended one year (OneYeari) or two or more years (TwoYeari) of pre-primary education. We added covariates to the model in order to reduce potential bias, including adolescents’ age and gender, a wealth index provided by PISA, maternal and paternal education, and age of entry to primary school. Furthermore, we included country and subnational (geographical region or school type) fixed effects to make within-country comparisons. We estimated separate models according to the World Bank’s categorization of income groups, i.e., for lower-middle-income countries (N = 6), upper-middle-income countries (N = 24), and high-income countries (N = 43), and region, i.e., East Asia and Pacific (N = 11), Europe and Central Asia (N = 42), Latin America and the Caribbean (N = 10), Middle East and North Africa (N = 8), and North America (N = 2). To obtain the COI for not reaching the SDG 4.2 targets for 1 year, we extend a procedure used to estimate the COI related to pre-primary schooling for five Latin American countries for the Lancet series on early childhood development21,43,45,46. In Eq. (1), the increase in individual earnings in future decades as a consequence of participating in preschool (PCIj x i, where PCIj is per capita income in year j and i is the causal impact of preschool on that income) is discounted by the discount rate d and summed over the relevant years in which earnings are expected to be affected (from when the individual starts to work a years after preschool through t years of working life) and then compared with the per child programme cost (c) for the N children covered by preschool (i) for the 2018 enrolments and (ii) if the SDG 4.2 targets were attained. The COI for each country therefore depends on projections for that country’s PCI, the impact of preschool on per capita income (i), the per child programme cost (c) and the expansion in enrolment in order to obtain the SDG 4.2 targets (100%−N). Note that this procedure probably leads to conservative estimates of the COI because only effects on adult earnings are included, but other short- and long-term impacts that are hard to monetize, such as reduced crime, are omitted. On the other hand, there may be general equilibrium effects that work in the opposite direction. Information on the discount rate (d), number of children affected (N), impact of pre-primary school on adult earnings (i) and cost (c) of pre-primary is critical for the simulations. As is common for many evaluations of social programmes where benefits accrue in the long term, in our simulations we used a discount rate (d) of 3%. For the current enrolments (N) we used the 2018 gross enrolment rates (GER) reported in UNESCO, Institute for Statistics47. For the SDG-targeted enrolments we used the maximum of 100% and the actual 2018 enrolments. The latter may be over 100% because the UNESCO data estimate the total enrolments of pre-primary students of all ages to the ratio of children in the country of pre-primary school ages, and there is catch-up in some countries with older children attending pre-primary school. We assume that such catch-up is of interest in attaining the SDG 4.2 targets. This use of the UNESCO GERs causes an underestimate of the COIs. The causal evidence on long-term effects of pre-primary school from randomized experiments in which children have been subsequently followed-up in their adult years is sparse, but suggests that impacts in earnings are substantial, of the order of 14% over the lifetime48. However, since that evidence comes from high-quality small-scale interventions targeting children from low socioeconomic backgrounds, it may not be externally valid in the case of lower-quality programmes, programmes implemented at scale or with children participating from all socioeconomic backgrounds. Thus, we have adopted a lower impact value of 8% for our simulations. The estimates for per child costs of existing pre-primary services, c, vary considerably across countries49,50. Since an important part of these variations reflects differences in wages and prices for services that relate to income levels across countries, we adjusted the programme costs for each group of countries based on the price level ratio of purchasing-power-parity (PPP) conversion factors that reflect the value of wages for services better than do market exchange rates. These assumptions are strong so the COI estimates are somewhat crude for any particular country. Supplementary Table 5 provides sensitivity analyses. COI do not change substantially if the assumed impact of pre-primary school on earnings decreases one or two percentage points, or if costs increase by 10% or 20%. If we apply higher discount rates (from 4% to 5%), patterns are similar but with smaller COI. Even though estimates would probably need to be refined to provide guidance for any particular country’s polices, they provide a useful order of magnitude for understanding an important component of the long-run global economic costs of lags in reaching the SDG 4.2 targets. Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Based on the provided description, the innovation recommendations to improve access to maternal health include:

1. Implementing comprehensive early childhood development programs: Prioritize quality early childhood care and education by adopting policies that support families and promote early learning for children. This can help address the substantial brain, cognitive, and socioemotional developments that occur in early life and have long-term impacts on education and well-being.

2. Increasing access to pre-primary education: Expand access to pre-primary education, ensuring that all children have the opportunity to attend quality early childhood development programs. This can be achieved through increased investment in infrastructure, teacher training, and outreach programs to reach marginalized communities.

3. Addressing inequalities in early childhood education: Recognize and address inequalities in achieving universal access to quality early childhood development, care, and preschool education. This includes targeting resources and interventions towards disadvantaged communities and populations to ensure equitable access to early childhood education.

4. Conducting research and data collection: Continue to gather data and conduct research on the impact of early childhood education on long-term outcomes, such as educational attainment and lifetime earnings. This can help inform policy decisions and resource allocation to support early childhood development programs.

5. Collaborating with international organizations: Work with international organizations, such as the United Nations and World Bank, to prioritize early childhood development and integrate it into global agendas, such as the Sustainable Development Goals. This can help mobilize resources and support for improving access to maternal health and early childhood education on a global scale.
AI Innovations Description
The recommendation to improve access to maternal health is to prioritize quality early childhood care and education. This recommendation is based on evidence that early childhood development and education have significant impacts on subsequent education and lifetime well-being. By adopting policies that support families and promote early learning, countries can reduce inequalities and improve access to maternal health.

To develop this recommendation into an innovation, stakeholders can consider implementing the following strategies:

1. Increase investment in early childhood care and education: Governments and organizations can allocate more resources to expand access to quality early childhood programs, including preschool education. This can involve building more early childhood centers, training and hiring qualified teachers, and providing financial support to families who cannot afford these services.

2. Strengthen collaboration between health and education sectors: To improve access to maternal health, it is important to integrate health services with early childhood care and education. This can involve establishing partnerships between healthcare providers, educators, and community organizations to ensure that pregnant women and new mothers receive the necessary support and information about maternal health.

3. Implement evidence-based interventions: It is crucial to implement evidence-based interventions that have been proven to improve maternal health outcomes. This can include providing prenatal and postnatal care, promoting breastfeeding, offering parenting education and support, and ensuring access to healthcare services for mothers and children.

4. Raise awareness and promote parental involvement: To ensure the success of early childhood care and education programs, it is important to raise awareness among parents and communities about the importance of early learning. This can involve conducting campaigns, organizing workshops and seminars, and providing resources and information to parents on how they can support their children’s development.

5. Monitor and evaluate the impact: It is essential to monitor and evaluate the impact of early childhood care and education programs on maternal health outcomes. This can involve collecting data on indicators such as maternal mortality rates, child development outcomes, and access to healthcare services. This information can help identify areas for improvement and guide future interventions.

By implementing these recommendations and innovations, countries can make significant progress in improving access to maternal health and ensuring the well-being of both mothers and children.
AI Innovations Methodology
Based on the provided description, the goal is to improve access to maternal health. Here are some potential recommendations for innovation in this area:

1. Telemedicine: Implementing telemedicine services can improve access to maternal health by allowing pregnant women to consult with healthcare professionals remotely. This can be particularly beneficial for women in rural or remote areas who may have limited access to healthcare facilities.

2. Mobile health applications: Developing mobile applications that provide information and resources related to maternal health can empower women to take control of their own health. These apps can provide guidance on prenatal care, nutrition, exercise, and track important milestones during pregnancy.

3. Community health workers: Training and deploying community health workers who have knowledge and skills in maternal health can help bridge the gap between healthcare facilities and pregnant women. These workers can provide education, support, and referrals to ensure that women receive the care they need.

4. Transportation solutions: Lack of transportation is a significant barrier to accessing maternal health services, especially in remote areas. Innovations in transportation, such as mobile clinics or community-based transportation services, can help overcome this challenge and ensure that women can reach healthcare facilities in a timely manner.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define indicators: Identify key indicators that measure access to maternal health, such as the number of prenatal visits, percentage of women receiving skilled birth attendance, or maternal mortality rates.

2. Collect baseline data: Gather data on the current state of access to maternal health services in the target population. This can include information on healthcare facilities, transportation infrastructure, and utilization of maternal health services.

3. Develop a simulation model: Create a simulation model that incorporates the recommended innovations and their potential impact on the identified indicators. This model should consider factors such as population demographics, geographic distribution, and existing healthcare infrastructure.

4. Input data and assumptions: Input relevant data into the simulation model, including information on the target population, the implementation of the innovations, and assumptions about their effectiveness and reach.

5. Run simulations: Run the simulation model to project the potential impact of the recommended innovations on access to maternal health. This can involve running multiple scenarios to assess different implementation strategies or variations in the target population.

6. Analyze results: Analyze the simulation results to understand the potential improvements in access to maternal health services. This can include assessing changes in the identified indicators and comparing different scenarios to identify the most effective strategies.

7. Refine and validate the model: Continuously refine and validate the simulation model based on real-world data and feedback from stakeholders. This iterative process will help improve the accuracy and reliability of the simulations.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of innovative interventions on improving access to maternal health and make informed decisions on implementation strategies.

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